STEM in Sustainable Development: A Competency-Based Corpus Analysis of UN Job Postings

STEM in Sustainable Development: A Competency-Based Corpus Analysis of UN Job Postings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article STEM in Sustainable Development: A Competency-Based Corpus Analysis of UN Job Postings Xianlong Zhu, Xifei Liu, Yanyan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7494569/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In the global sustainable development, more professionals are in demand in. However, little empirical research has examined the role of Science, Technology, Engineering, and Mathematics (STEM) and the competencies required in the positions of United Nations (UN) system. To address this gap, this study conducts a competency-based corpus analysis of 23,190 UN job postings (33.68 billion words) in the year of 2024, with the purpose of examining how STEM positions in UN recruitments align with sustainable development goals under the competency framework. Specifically, the paper investigates (1) the proportion of STEM-oriented vacancies; (2) differences between STEM and Non-STEM roles in job location, language requirements, and position levels; and (3) the competencies typically emphasized in STEM recruitments. Through corpus analysis and Latent Dirichlet Allocation (LDA) topic modeling, the study finds that (1) approximately 72.8% of UN postings are STEM-related, with strong emphasis on technical execution, data literacy, and innovative problem-solving; (2) STEM positions more frequently appear in Europe and North America with higher positions, where additional French or Spanish proficiency are usually required; (3) while professionalism, communication, and teamwork as universal requirements, quantitative reasoning emerges as particularly salient for STEM posts. The findings offer a data-driven foundation for the alignment between UN competency frameworks and the STEM education for sustainable development. Social science/Education Social science/Science technology and society Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In an era where the UN is tasked with addressing increasingly complex global challenges, from climate change and humanitarian crises to digital transformation, the alignment of workforce competencies with the demands of sustainable development has become a strategic priority. While existing scholarship has examined competency frameworks in broad terms, few studies have empirically mapped the actual skill requirements of UN positions to established models such as the United Nations Competencies for the Future (UNCF) and the 2030 Agenda for Sustainable Development(UN Office of Human Resources Management, 2009). This gap is particularly significant in distinguishing between STEM and non-STEM roles, as a clearer understanding of their distinct competency demands is essential for developing education and training pathways for Education for Sustainable Development (Kleinschmit et al., 2023). By leveraging large-scale corpus analysis and Latent Dirichlet Allocation (LDA) topic modeling of over 33 million words from UN job postings, this study provides an evidence-based understanding of how competencies are operationalized across job categories. The findings offer critical insights for education policy, curriculum design, and global workforce development, ensuring that talent pipelines are responsive to the evolving demands of sustainable development. Literature Review Previous competency frameworks for sustainable development Competency frameworks provide structured guidelines for defining the skills, knowledge, and attributes necessary for effective job performance. Research on competency frameworks have generally two main strands, i.e., the development and improvement of competency frameworks, and education to meet the needs of the competency frameworks (MacKay et al., 2024). The most classic and widely applicable generic competency framework is the Great Eight competency model for various jobs and organizations (Kurz & Bartram(2008); Bartram,(2005). Competency model is also hierarchical with the typical framework proposed by Organisation for Economic Co-operation and Development (OECD), including eight competencies at three levels of Performing, Inspiring, and Leading (Organisation for Economic Co-operation and Development, 2024). The OECD model is notable for its behaviorally anchored descriptions and multi-level progression, which make it adaptable across sectors, including international organizations. However, while its emphasis on innovation, collaboration, and ethics resonates strongly with the goals of sustainable development, the framework is primarily designed for intergovernmental policy contexts and may not fully target for sustainability in workplace. Among workplace-specific competency models in the field of sustainable development, the UNCF framework is designed to “create an organizational culture and environment that enables staff to contribute to their maximum potential”. It outlines three core values, eight core competencies, and six managerial competencies (Appendix A). Based on UNCF, UN agencies have published more specific competency frameworks, including UNHCR, UN-women, UNESCO, UNIDO, UNICEF, UNDP, and International Atomic Energy Agency (see Appendix A). To integrate multiple UN agency frameworks into a coherent analytical base, the present study synthesized recurring competencies into an adapted UNCF-aligned taxonomy (see Table 1). This taxonomy preserves the UNCF’s three-tier structure—core values, core competencies, and managerial competencies—while consolidating overlapping elements from nine major UN and related frameworks. Each competency cluster was assigned a coverage score, reflecting the number of frameworks in which it appears, and linked to a primary Sustainable Development Goal (SDG) to illustrate its relevance to sustainable development objectives. This synthesis highlights the competencies most consistently prioritized across the UN system and provides a benchmark for comparing corpus-derived recruitment demands with established institutional expectations. Table 1 Competency taxonomy (coverage-weighted) with SDG linkage. Category Synthesized competency cluster Coverage (of 9) Primary SDG link Core values Integrity / Ethics 8 SDG 16 Respect for diversity / Inclusion (incl. cultural & gender sensitivity) 7 SDG 10, SDG 5 Professionalism 6 SDG 8 Organizational commitment / Care / Trust 3 SDG 17 Core competencies – Collaboration & influence Communication 8 SDG 17 Teamwork / Collaboration 8 SDG 17 Partnership / Client orientation / Stakeholder engagement 7 SDG 17 Core competencies – Delivery & results Results focus / Accountability 6 SDG 16, SDG 8 Planning & organizing 5 SDG 9 Core competencies – Learning & innovation Continuous learning / Knowledge sharing 6 SDG 4 Creativity / Innovation 6 SDG 9 Adaptability / Resilience / Managing ambiguity 3 SDG 13 Core competencies – Technical & analytical Analytical thinking / Problem solving 4 SDG 9 Technological awareness / Scientific credibility / Information management 3 SDG 9 Managerial competencies Leadership (incl. leading & supervising) 7 SDG 17 Empowering others / People management / Capability building 6 SDG 17 Vision / Strategic thinking 5 SDG 17 Managing performance 5 SDG 8 Decision-making / Judgment 4 SDG 16 Change management 4 SDG 9, SDG 17 Partnership building (managerial) 3 SDG 17 Trust building 2 SDG 16 Moreover, the frameworks mentioned above have not been fully tested for validity in real UN recruitment, leaving a gap in understanding how well these frameworks align with actual competency demands in practice. Actual competency demands are mostly examined through data-driven approach in previous research of industry-specific hiring trends (Geagea & MacCallum, 2020; Homberg et al., 2020; Aleksić et al., 2022; Kart & Şimşek, 2024) . In particular, as science- and technology-intensive fields of sustainable development, STEM-related roles warrant urgent investigation, given their central role in addressing complex global issues. This gap highlights the need for evidence-based approaches, such as corpus analysis, to investigate how competencies are represented in UN job postings and whether these align with evolving organizational priorities. This alignment is critical for both policy-makers and education practitioners seeking to prepare talent for careers concerning sustainable development (Quendler & Lamb, 2016). Integrating sustainable development and STEM education As an essential component of sustainable development, STEM education combines scientific inquiry, technological application, engineering design, and mathematical reasoning to equip learners with the capacity to tackle complex, real-world challenges through interdisciplinary and solutions-oriented approaches (Aslam et al., 2022; Cai et al., 2023). Recent scholarship links STEM’s problem-solving orientation to addressing global sustainability challenges, however, relatively few studies examine whether STEM programs develop the competencies most relevant to professional contexts, particularly those requiring interdisciplinary collaboration (Gao et al., 2020). Education for sustainable development emphasizes the integration of sustainability principles into curricula, particularly in science and engineering disciplines (Uralovich et al., 2023). Global policy milestones, such as Our Common Future (1987), Agenda 21 (1992), and the United Nations Sustainable Development Goals (2015), have underscored the need for scientific and technological expertise to address environmental, social, and economic challenges. In this context, STEM graduates are increasingly expected to apply their expertise to advancing sustainable development and addressing its environmental, social, and economic dimensions (Lozano et al., 2017). Recognizing the importance of STEM expertise, the UN and its agencies seek professionals capable of addressing complex global challenges (Pérez-Foguet et al., 2018). Existing research on education for sustainable development has highlighted the importance of integrating sustainability principles into science and engineering curricula (Lin et al., 2023). Most studies on sustainability-related careers emphasize broad skills such as adaptability and cross-sector collaboration (Spencer & Spencer, 1993; Lozano et al., 2017). Field-specific examples include Heaslip et al. (2019), which developed a T-shaped humanitarian logistics competency framework, and Shin et al. (2025), which applied LDA topic modeling to examine sustainable ethics discourse. However, much of the literature on STEM in education for sustainable development remains theoretical, with limited empirical validation against actual STEM-related position needs (Bybee, 2010; Breiner et al., 2012). While conceptual competency frameworks describe desired competencies, they do not necessarily capture how recruitment prioritize these competencies in practice. This is particularly salient for international organizations such as UN, where STEM expertise is central to achieving SDGs. This study addresses this gap by examining the competencies explicitly sought in UN job postings and assessing their alignment with established frameworks for sustainability-focused STEM careers. Based upon the literature review, the study aims to address the following research gaps. First, although STEM is widely acknowledged as essential to sustainable development, few studies connect STEM education and the recruitment practices of international organizations such as the UN. In particular, little is known about how educational preparation aligns with the specific competency requirements for sustainable development. Second, while competency frameworks are widely used in UN systems, they are more likely to be top-down models. Such measures, including expert consultations and survey-based evaluations, are likely to fail to capture how international organizations in UN system actually prioritize competencies in recruitment . Accordingly, the research seeks respond tothese gaps by examining the following research questions (RQs). RQ1: What is the distribution of STEM and non-STEM roles in UN system in terms of job proportions, locations, language requirements, and position levels? RQ2: How are competencies required for STEM roles in UN system, as they emerge from the corpus and compared with UNCF? Drawing on a 33.68-million-word corpus of UN job postings, this study identifies the competencies most frequently required for STEM-related sustainability roles and compares them with the UNCF framework. The findings will (1) provide empirical evidence on STEM competency demands in the UN system, (2) inform the refinement of competency frameworks to better reflect sustainability-focused roles, and (3) offer actionable insights for policymakers and educators to align training with workforce needs in global governance. Methods Research design This study aims to provide an empirical, data-driven evaluation of competency expectations in STEM-related sustainability careers. Instead of relying on static competency models, this research systematically quantifies which competencies are most frequently required across different roles and how these competencies align with the UN competency frameworks. To address RQ1, a descriptive analysis is conducted to quantify the prevalence of STEM-related job postings across UN agencies. The proportions, locations, language requirements, and position levels of STEM positions are calculated and visualized by Python 3.13. This step establishes a baseline understanding of STEM representation in the UN system, providing essential context for the competency analysis in RQ2. To tackle RQ2, the targeted sections describing competency requirements were extracted from the whole texts, and then an LDA model is used to identify topics of competencies. This method enables a comparison of the LDA outcomes with the UNCF framework, which includes key competencies such as Professionalism, Communication, Teamwork, Leadership, and Innovation, and identifies the gap between competencies required in real recruitment and the counterpart in existing frameworks. Data collection and classification This study collected recruitment posts via Python 3.13 from uncareer.net, a widely used aggregator of UN vacancies. Each posting contains structured metadata, including “Date,” “Organization”, “Country”, “City”, “Post Level”, and the full job description. To ensure corpus validity, postings were included only if they provided complete metadata and a full English text version. Duplicates were removed, and non-recruitment content (such as website headers and footers) was excluded. In total, 23,190 postings from 2024 were collected, forming a 33.68-million-word corpus. This procedure follows corpus-linguistics principles of authenticity, representativeness, and systematic sampling (Baker, 2006; McEnery & Hardie, 2012), thereby ensuring that the corpus accurately reflects UN recruitment discourse. To distinguish between STEM posts and non-STEM posts, the study utilized the U.S. Department of Homeland Security STEM Designated Degree Program (DHS) List (2024) as the keywords for Python selection. This list, widely recognized in educational and labor market research, provides a standardized and periodically updated classification of STEM disciplines, thereby ensuring methodological transparency and cross-study comparability (U.S. Immigration and Customs Enforcement, 2024). Keywords from this list were refined to improve retrieval accuracy, including the removal of non-specific terms and minor linguistic adjustments (see Appendix B for full keyword processing steps). Data preprocessing As UN posts are composed of English and non-English versions, we’ve built a word corpus containing all potential words in the study, ranging from A1 to C2 position levels. To collect the words, we’ve accessed all words and phrases in an online CFER English vocabulary profile(English Profile, 2023), as well as Oxford 3000, Oxford 5000, and Longman Communication 3000. The English text data underwent standard preprocessing, i.e., (1) removal of URLs and stopwords, lowercasing, (2) tokenization using NLTK, part-of-speech tagging, and retention of nouns, adjectives, and adverbs. Bi-grams were identified using the Phrases function in Gensim, the open-source Python library for topic modeling (Řehůřek & Sojka, 2010). Topic modeling Preprocessed text was converted into numerical vectors using Gensim’s corpora module (Řehůřek & Sojka, 2010). LDA, a probabilistic method for uncovering latent thematic structures in text (Blei et al., 2003), was applied to identify clusters of related competencies, with recent advances further improving efficiency and coherence (Song et al., 2025; Sarker et al., 2025). To remove vocabularies with minimal impact from the LDA model, this research only chose 90% percentile used to filter out the top 10% of words with the highest weight, which are usually the core words of the topic. Results RQ1: Distributions of STEM and non-STEM roles Proportion of STEM vs. Non‑STEM Posts Of the 22174 postings with sufficient classification information, 16142 (72.8%) were STEM roles and 6032 (27.2%) were non-STEM. This significant STEM share emphasize the scientific and technical orientation of the UN workforce, reflecting UN’s reliance on STEM expertise in sustainable development. STEM-related roles covered a wide range of disciplines, with the largest concentrations in planetary sciences ( n =276), information and communications technology ( n =212), computer science ( n =206), agroecology/agriculture ( n =162), and transportation engineering ( n =157). The range of disciplines reflects the UN’s multidisciplinary approach to achieving the SDGs, where expertise in environmental sciences supports climate action and resource management, digital technologies enable data-driven decision-making and innovation, and infrastructure development underpins resilient cities, transportation networks, and essential services in both humanitarian and development contexts. A full list of educational requirements for STEM positions is provided in Appendix C. Locations of STEM and Non-STEM Posts The geographic distribution of UN recruitment reveals distinct patterns between STEM and non-STEM roles are shown in Table 2 and visualised in Fig. 1. For STEM positions, the highest concentrations were in Switzerland (10.82%) and the United States (10.69%), reflecting the presence of major UN headquarters and specialized agencies in Geneva and New York. Other high-ranking countries for STEM recruitment included France, Sudan, Nigeria, Kenya, Ukraine, Ethiopia, the United Kingdom, and India. Non-STEM positions showed a different profile, with Germany (9.80%) at the top, followed by the United States, Switzerland, Colombia, and France. Several Latin American countries (e.g., Colombia, Mexico, Ecuador, Spain) appeared in the top 10 for non-STEM recruitment, suggesting a stronger regional spread for administrative and programmatic roles. This result is consistent with how the UN system allocates work and offices. Germany, the U.S., and Switzerland host major UN headquarters or specialized agencies (e.g., UN Bonn campus in Germany, UN Secretariat and agencies in New York, WHO in Geneva). Overall, STEM recruitment exhibited a dual geographic concentration. The first cluster comprised high-income administrative and policy hubs, such as Switzerland, the United States, France, and the United Kingdom. These countries typically serve as centres for global technical coordination and strategic planning. The second cluster encompassed field operation centres in developing or crisis-affected regions, including Sudan, Nigeria, Kenya, Ethiopia, Ukraine, and India, where engineering, health, infrastructure, and environmental expertise are essential to implementing the UN’s sustainable development agenda and humanitarian operations. Table 2 Job l ocations of STEM and n on-STEM p osts . STEM Non-STEM Ranking Country Number Proportion Ranking Country Number Proportion 1 Switzerland 1476 10.82% 1 Germany 522 9.80% 2 United States 1459 10.69% 2 United States 280 5.26% 3 France 413 3.03% 3 Switzerland 264 4.96% 4 Sudan 389 2.85% 4 Colombia 251 4.71% 5 Nigeria 386 2.83% 5 France 234 4.39% 6 Kenya 373 2.73% 6 Mexico 134 2.52% 7 Ukraine 361 2.65% 7 Democratic Republic of Congo 127 2.38% 8 Ethiopia 314 2.30% 8 Ecuador 121 2.27% 9 United Kingdom 314 2.30% 9 Ukraine 121 2.27% 10 India 233 1.71% 10 Spain 118 2.22% Language Requirements of STEM and Non-STEM Posts Table 3 shows that English dominates recruitment across both STEM and non-STEM roles, appearing in over half of all postings (55.41% overall). French is the second most requested language (19.10% overall), with slightly higher demand in STEM positions (19.76%) than in non-STEM (14.55%), followed by Spanish, Arabic, Russian, and Chinese. The total counts exceed the number of posts because many vacancies require more than one official UN language. This is especially pronounced in STEM roles, where the combined ratio of language requirements (136.25%) far surpasses that of non-STEM roles (52.65%), indicating stronger multilingual expectations in technical and field-based functions. Table 3 Languages required in STEM and non-STEM recruitment. Ranking Language Required Total STEM Non-STEM Number Percentage Number Percentage Number Percentage 1 English 13945 55.41% 12059 54.83% 1886 59.38% 2 French 4808 19.10% 4346 19.76% 462 14.55% 3 Spanish 2713 10.78% 2357 10.72% 356 11.21% 4 Arabic 2131 8.47% 1815 8.25% 316 9.95% 5 Russian 1040 4.13% 913 4.15% 127 4.00% 6 Chinese 532 2.11% 503 2.29% 29 0.91% All Languages 25169 100% 21993 100% 3176 100% All Posts 22174 113.50% 16142 136.25% 6032 52.65% These patterns reflect the UN’s operational footprint, particularly in Francophone Africa and Geneva- and Paris-based agencies where French serves alongside English as a working language. The higher multilingual demand for STEM posts suggests that technical experts often work in contexts requiring close engagement with local stakeholders and regional offices. In the synthesis competency framework, such language skills strengthen “Collaboration & Influence” competencies, especially communication and stakeholder engagement, and reinforce “Respect for Diversity/Inclusion” as a core value, i.e., positioning multilingualism as a strategic enabler of effective, culturally responsive UN operations. This shows a feature of “facilitating communication between linguistically and culturally different parties”(Tse, 1996). Post Levels of STEM and Non-STEM roles The distribution of STEM and non-STEM positions varies across professional levels (Fig. 2 and Table 4). Percentages reflect the relative share within each category. Higher-level positions (General Support to Director/Top Executive) are dominated by STEM roles, reflecting increasing demands for synthesis-framework competencies in the Technical & Analytical, Delivery & Results, and Managerial clusters, linked primarily to SDG 9 (Industry, Innovation and Infrastructure) and SDG 17 (Partnerships for the Goals). In contrast, internships are more accessible to non-STEM applicants, drawing on Core Values and Collaboration & Influence competencies that are broadly transferable across disciplines. Table 4 Numbers and p roportions of p ost l evels between STEM and n on-STEM p osts . Rank Types of Positions STEM N (%) Non-STEM N (%) 1 Internship 575 (44.71%) 711 (55.29%) 2 General Support 281 (71.68%) 111 (28.32%) 3 Entry Professional 388 (67.95%) 183 (32.05%) 4 Mid-level Professional 352 (73.64%) 126 (26.36%) 5 Chief and Senior Professional 252 (77.06%) 75 (22.94%) 6 Director and Top Executive 463 (70.05%) 198 (29.95%) RQ2: Competencies emphasized for STEM and non-STEM positions To find the optimized number of topics (“K”), we’ve explored the complexity and credibility of different types of posts, iterating 100 times. We finally chose 18 topics for STEM posts and 8 topics for Non-STEM posts. As shown in Fig. 3, the number of topics was determined by balancing coherence (higher is better) and perplexity (lower is better). For STEM posts, 18 topics achieved relatively high coherence with stable perplexity, while for Non-STEM posts, 8 topics provided the best balance, thus avoiding overfitting while maintaining interpretability. In the LDA model building, the data was iterated 300 times. These findings are interpreted in light of the UNCF framework and the 2030 Agenda for Sustainable Development. LDA topic modeling for competencies required by STEM posts The LDA analysis of STEM job postings reveals a well-differentiated set of competency demands, reflecting the multifaceted nature of technical work in the UN system. The 18 topics extracted range from highly specialised areas such as climate change mitigation, engineering practice, and cybersecurity to cross-cutting skills in programme management, legal expertise, multilingual communication, and respect for diversity. The prevalence of both domain-specific and transferable competencies underscores that STEM roles in the UN require a blend of advanced technical knowledge, sector-specific experience, and the ability to operate effectively in multicultural, interdisciplinary contexts aligned with the UNCF framework and the 2030 Agenda. Topic 1 (Agriculture and Food Security), constituting approximately 4.08% of the analyzed corpus, revolves around “security,” “agriculture”, “humanitarian”, and “nutrition”. This topic is highly related to specific majors on the DHS List and requires applicants’ identification of humanitarian principles to conform to the United Nations working environment. Representing 7.03% of the corpus, Topic2 (Financial Analysis) focuses on financial analysis and accounting skills. Dominant terms like “financial”, “accounting”, and “business” denote competencies in budgeting, auditing, and financial reporting. At 4.61% of the corpus, Topic3 (Program Management) emphasizes terms like “program”, “fluency”, and “communication”, together with “leadership” “master”, demanding high education level in program management and professional competencies for communication. Constituting 4.75% of the corpus, Topic4 (Climate Change and Environmental Protection) targets competencies in ecological governance. Terms like “climate”, “environmental”, and “sustainable” highlight skills in carbon mitigation and green policy drafting, which is related to specific majors. At 3.50% of the corpus, Topic5 (Cybersecurity and Digital Infrastructure Management) prioritizes technical and operational competencies in IT systems with terms “security”, “network”, “science” and “web”. This means that candidates should handle with cyber risk and IT system administration. Representing 3.61% of the corpus, Topic6 (Economic Engineering) merges financial and technical competencies. With terms stressing financial profession including “economics”, “finance”, and terms stressing education level and management like “administration” “master” “professional”, this topic has strong indication of higher managerial level of STEM positions. Topic 7 (Business and Communication) represents 6.25% of the corpus, with components like “business”, “communication” “economics”, and “leadership”. This means candidates should understand core business concepts and demonstrate leadership in group tasks. Topic 8 (Respect for Diversity), accounting for 5.05% of the corpus, emphasizes ethical governance and intercultural adaptability. This topic has unique key words including “diversity”, “commitment”, “sensitivity”, “respect”, “willingness”, and “accountability”, setting up moral codes in accordance with the United Nations core values. Representing 6.13% of the corpus, Topic 9 (Humanitarian Coordination and Crisis Response) has dominant terms like “humanitarian” and “coordination” emphasize competencies in disaster preparedness and interagency collaboration. To achieve this, “language”, “fluency” and “response” are required as basic skills. Topic 10 (Digital Media Proficiency and Technical Communication), takes up 4.93% of the corpus, “media”, “software”, “digital” and “design” reflect digital literacy in multimedia production. Employees for such positions indicate expertise in online platforms and “journalism” to enhance the United Nations influence in the digital era and construct positive images for their working agencies. Constituting 9.25% of the corpus, Topic 11 (Dada Processing and Statistical Analysis) is composed of “data”, “analysis”, “software”, “collection”, which means a good command of databases, data warehousing, and data governance. Topic 12 is Basic Office skills, taking up 6.31% of the corpus. Employees are able to use “Excel”, and fluent in both “oral” and “writing” working language to have “communication”, as well as withstanding “pressure”. Representing 6.05% of the corpus, Topic 13 (Business Strategy and Administration) combines strategic planning and relationship management. It has key words including “business”, “administration”, “fluency”, and “communication”. Topic 14, Technical Background, representing 5.71% of the corpus, focuses on skill transfer and institutional strengthening. Key terms consist of “technical”, “evaluation”, “capacity” and “training”, which emphasizes receiving education in science and technology. Topic 15 (Engineering Background) constitutes 6.63% of the corpus. This topic targets engineering excellence with “engineering, “application”, “eligible”, “design” and “software”. This dimension is related to candidates’ ability of engineering practice. At 8.73% of the corpus, Topic 16 (Legal Language Competency) merges “law” and “legal” in legal expertise, and “language” and “professional” in language proficiency. This indicates the applicants should be proficient in working language and mastering knowledge in required legal field. Topic 17 (Technical Language Proficiency), representing 4.27% of the corpus, addresses hands-on technical roles. Terms such as “language”, “professional”, “completion”, and “secondary” target candidates in skilled trades or technical roles. Topic 18 (Community-Based Technical Governance), at 3.12% of the corpus, this topic centers on green initiatives and participatory governance with key terms “community”, “energy”, “nutrition” and “manager”. An overview of all 18 topics derived from the LDA modeling of STEM job postings, including their proportions and key terms, is provided in Table 6. Table 6 Results of LDA t opic modeling for STEM posts . Topic Proportion Content (Weight) Topic1: Agriculture and Food Security 4.08% security (0.149) + agriculture (0.038) + humanitarian (0.035) + communication (0.027) + legal (0.023) + nutrition (0.021) + fluency (0.021) + leadership (0.019) + law (0.018) + professional (0.017) Topic2: Financial Analysis 7.03% financial (0.126) + finance (0.078) + accounting (0.077) + business (0.033) + administration (0.031) + professional (0.029) + analysis (0.027) + communication (0.026) + oral (0.018) + language (0.017) Topic3: Program Management 4.61% programme (0.055) + fluency (0.040) + communication (0.038) + technical (0.032) + expertise (0.030) + leadership (0.029) + master (0.028) + population (0.026) + professional (0.023) + prevention (0.022) Topic4: Climate Change and Environmental Protection 4.75% climate (0.101) + environmental (0.068) + communication (0.036) + science (0.030) + agricultural (0.026) + sustainable (0.023) + environment (0.022) + oral (0.021) + capacity (0.020) + master (0.020) Topic5: Cybersecurity and Digital Infrastructure Management 3.50% security (0.056) + relations (0.040) + network (0.030) + communication (0.027) + science (0.026) + environment (0.023) + additional (0.022) + web (0.022) + computer (0.019) + technical (0.017) Topic6: Economic Engineering 3.61% economics (0.049) + engineering (0.049) + master (0.041) + additional (0.032) + science (0.030) + administration (0.027) + finance (0.026) + invest (0.026) + professional (0.024) + environmental (0.024) + business (0.024) Topic7: Business and Communication 6.25% business (0.046) + communication (0.025) + economics (0.025) + financial (0.022) + environment (0.020) + leadership (0.018) + analysis (0.017) + senior (0.017) + external (0.016) + professional (0.015) Topic8: Respect for Diversity 5.05% communication (0.035) + diversity (0.034) + commitment (0.028) + environment (0.028) + respect (0.027) + sensitivity (0.024) + willingness (0.022) + training (0.019) + values (0.019) + accountability (0.016) + initiative (0.016) Topic9: Humanitarian Coordination and Crisis Response 6.13% humanitarian (0.141) + coordination (0.041) + professional (0.040) + relations (0.037) + fluency (0.032) + communication (0.027) + response (0.026) + language (0.021) + science (0.020) + emergency (0.017) Topic10: Digital Media Proficiency and Technical Communication 4.93% media (0.070) + communication (0.049) + professional (0.046) + software (0.028) + digital (0.028) + design (0.027) + language (0.021) + relations (0.019) + video (0.017) + journalism (0.017) Topic11: Dada Processing and Statistical Analysis 9.25% data (0.163) + analysis (0.066) + software (0.036) + collection (0.030) + science (0.027) + statistics (0.022) + communication (0.021) + evaluation (0.018) + computer (0.017) + statistical (0.016) + technical (0.016) Topic12: Basic Office skills 6.31% communication (0.051) + environment (0.040) + excel (0.028) + attention (0.024) + discretion (0.022) + oral (0.022) + pressure (0.019) + initiative (0.018) + suite (0.017) + writing (0.017) Topic13: Business Strategy and Administration 6.05% business (0.097) + administration (0.081) + communication (0.043) + fluency (0.021) + professional (0.020) + language (0.018) + excel (0.017) + administrative (0.016) + environment (0.014) + oral (0.014) Topic14: Technology Background 5.71% technical (0.078) + evaluation (0.044) + capacity (0.033) + training (0.022) + communication (0.020) + design (0.016) + fluency (0.016) + software (0.015) + master (0.014) + cultural (0.014) Topic15: Engineering Background 6.63% professional (0.058) + engineering (0.047) + application (0.041) + eligible (0.037) + master (0.034) + design (0.033) + associate (0.032) + software (0.031) + electrical (0.030) + technical (0.027) Topic16: Legal Language Competency 8.73% language (0.182) + professional (0.091) + law (0.068) + support (0.028) + legal (0.026) + environment (0.022) + training (0.016) + programme (0.016) + specific (0.015) + science (0.015) Topic17: Technical Language Proficiency 4.27% language (0.081) + professional (0.027) + completion (0.026) + secondary (0.024) + certificate (0.023) + technical (0.022) + communication (0.022) + fluency (0.022) + oral (0.018) + support (0.017) Topic18: Community-Based Technical Governance 3.12% community (0.057) + energy (0.048) + nutrition (0.032) + manager (0.023) + technical (0.023) + communication (0.023) + scope (0.023) + department (0.021) + technology (0.019) + leadership (0.018) The Intertopic Distance Map (Fig. 4) illustrates the distribution of all 18 topics extracted from the LDA modeling of STEM posts, confirming that the topics are well separated and conceptually distinct. Fig. 4 highlights the most significant topic as an example, showing its clear spatial distinction while maintaining moderate proximity to related clusters. LDA Topic Modeling for competencies required by Non-STEM Posts LDA topic modeling of non-STEM positions identifies eight distinct competency clusters that span functional expertise and cross-cutting skills. These topics highlight the importance of communication, cultural adaptability, ethical conduct, and leadership alongside domain-specific abilities in finance, media, and digital design. These competencies equip professionals to navigate complex organizational environments, foster inclusive collaboration, and contribute effectively to the UN’s operational priorities and sustainable development objectives. Topic 1 (Cross-Cultural Program Communication), constituting 14.66% of the corpus, emphasizes competencies in cross-cultural communication and regulatory adherence with key terms like “program”, “communication”, and “language”. Topic 2 (Humanitarianism), constituting 17.18% of the corpus with key terms “humanitarian” “fluency”, “technical”, “emergency”, and “support”, setting standards for candidates to understand core humanitarian principles. Representing 17.19% of the corpus, Topic 3 (Finance and Accounting) focuses on fiscal accuracy and procedural compliance. The keywords “financial”, “communication”, “accounting”, and “finance” indicate the competency of related financial knowledge. Topic 4(Professional Language Proficiency), constituting 20.61%, the largest proportion, of the corpus, this topic has terms “language”, “business”, “finance”, “professional”, and “administration”. Candidates are competent for professional negotiation. At 5.53% of the corpus, Topic 5 (Media and Social Responsibility) has keywords “media”, “energy”, “environment”, “exploitation”, “master”, and “society”. This means employees should be aware of the social impact on media and take related responsibilities. Topic 6 (Commitment and Diversity) represents 9.62% of the corpus, with terms “commitment”, “communication”, “diversity”, “integrity”, “values”, and “cultural” in ethical decision-making and cross-cultural communication. At 10.28% of the corpus, Topic 7 (Design Skills and Responsibility) emphasizes digital creativity and devotion to their designing works. Terms like “design”, “online”, and “media” reflect expertise in digital content creation, and “security” “volunteer” and “responsible” link to respect for the position. Topic 8 (Leadership and Professionalism). Constituting 8.71% of the corpus, this topic targets higher-level competencies with terms like “leadership”, “senior”, “society”, “master”, and “professional”. Table 7 summarises the eight topics identified from the LDA analysis of non-STEM job postings, outlining their relative proportions and the principal terms associated with each theme. Table 7 Results of LDA t opic modeling for n on-STEM posts . Topic Proportion Content (Weight) Topic1: Cross-Cultural Program Communication 14.66% programme (0.052) + communication (0.049) + language (0.044) + law (0.031) + professional (0.028) + environment (0.027) + relations (0.023) + training (0.019) + media (0.018) + cultural (0.017) + computer (0.017) Topic2: Humanitarianism 17.18% humanitarian (0.113) + fluency (0.034) + technical (0.028) + emergency (0.026) + support (0.024) + leadership (0.023) + communication (0.023) + expertise (0.020) + manager (0.018) + coordination (0.018) + security (0.018) + response (0.018) Topic3: Finance and Accounting 13.40% financial (0.077) + communication (0.041) + accounting (0.031) + attention (0.027) + finance (0.026) + excel (0.026) + pressure (0.021) + reporting (0.017) + data (0.017) + professional (0.017) Topic4: Professional Language Proficiency 20.60% language (0.062) + business (0.051) + finance (0.045) + professional (0.040) + administration (0.035) + financial (0.034) + accounting (0.030) + fluency (0.026) + software (0.022) + master (0.021) Topic5: Media and Information Sensitivity 5.54% media (0.038) + energy (0.032) + environment (0.022) + exploitation (0.019) + master (0.018) + society (0.017) + community (0.017) + capacity (0.017) + humanitarian (0.016) + respect (0.016) Topic6: Commitment and Diversity 9.60% commitment (0.042) + communication (0.034) + diversity (0.027) + integrity (0.027) + values (0.022) + cultural (0.022) + orientation (0.020) + awareness (0.019) + planning (0.019) + accountability (0.018) Topic7: Design Skills and Responsibility 10.28% design (0.041) + online (0.033) + media (0.030) + security (0.024) + volunteer (0.023) + technical (0.022) + responsible (0.020) + digital (0.019) + support (0.019) + video (0.016) Topic8: Leadership and Professionalism 8.75% leadership (0.052) + senior (0.033) + fluency (0.025) + communication (0.023) + master (0.023) + professional (0.023) + society (0.021) + oral (0.019) + climate (0.019) + capacity (0.019) The Intertopic Distance Map (Fig. 5) presents the spatial distribution of all eight topics derived from LDA modeling of non-STEM posts, showing clear separation and minimal overlap, which indicates strong topic coherence. The most prominent topic is shown here as an illustrative example, demonstrating how the model captures distinct thematic clusters while maintaining meaningful relationships among them. Discussion STEM as the operational core of UN sustainable development efforts The finding that approximately 72.8% of UN job postings in 2024 are STEM-designated underscores the UN system’s strong reliance on technical and analytical capabilities to achieve sustainable development. This aligns with the view that STEM expertise is foundational to “Industry, Innovation and Infrastructure” (SDG 9) and “Climate Action” (SDG 13) (UN, 2015). The broad distribution of STEM roles—from Geneva and New York headquarters to field operations in Africa and Asia—illustrates a dual operational model that combines global coordination with local implementation, supporting Liu et al.’s (2024) argument that STEM functions form the operational backbone of international institutions. Notably, STEM positions outnumber their non-STEM counterparts by a significant margin, with higher average grades and greater promotion prospects. This finding is consistent with Zhang et al.’s (2024) conclusion that the employment prospects of STEM undergraduates generally exceed those of graduates overall. It reflects that one of the most significant interests of STEM career, the outcome expectations (Jiang et al., 2024), can be fulfilled in an international organizations where non-STEM competencies such as language, communication, and management are valued. Projections from the U.S. Bureau of Labor Statistics (2025) indicate that STEM employment will grow by 10.4% by 2033, which nearly triple the growth rate for non-STEM roles (3.6%), and the present study provides clear evidence that this trend is also visible within the UN system. Multilingualism as an essential competency English dominates across all postings (55%), but the substantially higher combined language requirement in STEM roles (136%) compared to non-STEM (53%) indicates that multilingualism functions as a core enabler rather than an optional skill. This aligns with the “Collaboration & Influence” cluster in the synthesized competency taxonomy, as well as research highlighting language proficiency as a prerequisite for effective cross-cultural collaboration in international contexts (Quendler & Lamb, 2016). It also supports Lozano et al.’s (2017) view that inclusiveness is a foundational capability within sustainability-oriented careers. In STEM-focused UN roles, multilingual capability often extends beyond formal communication to encompass technical knowledge transfer, field coordination, and stakeholder negotiation in linguistically diverse environments. This is particularly relevant in field missions in Francophone Africa, the Arab region, and parts of Latin America, where STEM specialists must engage with local authorities, partner agencies, and communities in languages other than English. Candidates from less multilingual regions may therefore face structural disadvantages in competing for STEM positions with significant field engagement. Addressing this gap requires early integration of language training into STEM curricula. This integration should be combined with intercultural communication training, as recommended in Pérez-Foguet et al (2018)to prepare graduates for the complex socio-technical demands of sustainable development projects. Ultimately, multilingualism in STEM roles mean building trust, ensuring accurate technical exchange, and fostering inclusive participation in problem-solving processes central to the UN’s sustainable development mandate. Technical depth coupled with delivery & managerial competencies The distribution of positions across professional levels reveals a marked structural difference between STEM and non-STEM roles in the UN system (Fig. 2; Table 4). STEM positions are consistently overrepresented at higher professional grades, with 73.6% of mid-level, 77.1% of senior professional, and 70.1% of director and top executive posts falling within STEM domains. By contrast, non-STEM positions dominate internship opportunities (55.3%), suggesting that initial entry points into the UN system are more accessible for candidates from non-technical fields. This pattern indicates that the pathway to senior leadership in the UN increasingly depends on mastery of both advanced technical competencies and higher-order managerial capabilities, a trend also observed in sustainable development. At the senior levels, STEM roles demand an integrated portfolio of competencies combining Technical & Analytical capabilities (e.g., technological awareness, scientific credibility, advanced data analytics) with Delivery & Results competencies (planning, accountability, resource mobilisation) and Managerial competencies (vision, decision-making, people management) as defined in the UNCF framework. This integration reflects the UN’s operational imperative to address complex, technology-driven mandates in areas such as climate adaptation, digital infrastructure, and global health security—sectors closely linked to SDG 9 (Industry, Innovation and Infrastructure), SDG 13 (Climate Action), and SDG 17 (Partnerships for the Goals). Similar competency configurations have been documented in leadership profiles for sustainability-oriented organisations, where technical domain mastery must be matched with adaptive leadership and systems thinking (Wiek et al., 2011). Compared with findings from a recent study, an importance sequence followed by technical theory, engineering design, leadership, communication, problem solving, professionalism, and lifelong learning (Yu et al., 2022) with its Generic Engineering Competencies Questionnaire (GECQ) method, our study showed higher importance in professionalism and lower in engineering design. LDA topic modelling provides further granularity, showing that STEM leadership roles often span interdisciplinary domains. For example, “Economic Engineering” (Topic 6) merges financial and technical decision-making, while “Cybersecurity and Digital Infrastructure Management” (Topic 5) integrates risk governance with technological resilience. These hybrid competency profiles reflect the evolving nature of UN mandates, where leadership involves orchestrating diverse technical teams under political, cultural, and operational constraints. Non-STEM leadership topics, such as “Cross-Cultural Program Communication” and “Commitment and Diversity”, reveal an enduring emphasis on linguistic agility, cultural intelligence, and ethical governance, which remain indispensable for sustaining multilateral collaboration in complex geopolitical environments (Ang et al., 2020). Gaps between institutional frameworks and recruitment reality It is proved that gaps still exist between STEM education and the required workplace skills, and further researches are focusing on discovering whether frameworks or educational patterns could cover all competencies required in real STEM positions (Zhan et al., 2023). While the synthesized UNCF-aligned taxonomy includes Creativity/Innovation and Trust-Building, those competencies appear less explicitly in recruitment language. Instead, innovation seems embedded in data and digital skill descriptors, and trust-building in coordination or stakeholder engagement terminology. This echoes Wiek & Redman’s (2022) caution that top-down competency frameworks may not fully reflect how organizations operationalize those competencies. It suggests a need for frameworks like UNCF to articulate innovations and relational leadership more directly in job descriptors. Implications Policy-making for education for sustainable development The UN job posting analysis underscores that Education for Sustainable Development policy must be more closely aligned with real-world workforce needs in international organizations. The high demand for STEM expertise, particularly in environmental sciences, information and communications technology, and infrastructure, paired with transversal competencies such as multilingualism, cross-cultural communication, and ethical governance, suggests that Education for Sustainable Development policy frameworks should move beyond content knowledge to emphasize integrated competency profiles. Policymakers at both national and intergovernmental levels can use these insights to embed UN-relevant competencies into national Education for Sustainable Development strategies, thereby preparing graduates for active participation in global sustainability governance. This requires cross-ministerial coordination between education, foreign affairs, and science & technology departments to ensure that technical training is accompanied by value-based and intercultural competencies. Competency-based curriculum and training The findings reveal a clear need for competency-based curriculum design that prepares graduates for hybrid roles in the UN system. In STEM fields, curricula should combine domain expertise with skills in negotiation, policy interpretation, and sustainable innovation. In non-STEM fields, programs should integrate digital literacy, data-informed decision-making, and a foundational awareness of science–policy interfaces. Training providers—whether universities, vocational institutes, or international training academies—should adopt modular, outcomes-oriented designs linked directly to the UN Competencies for the Future (UNCF) framework. Partnerships between higher education institutions and UN agencies can create practice-oriented learning environments, such as simulation-based policy labs or multilingual field assignments, ensuring that graduates possess both the technical depth and the intercultural agility required for UN service. Theoretical framework based on bottom-up research methods This study demonstrates how bottom-up, data-driven approaches such as corpus linguistics and LDA topic modeling can enhance existing theoretical frameworks for competency development in global governance. Rather than relying solely on prescriptive models, the analysis of authentic job postings reveals how competencies evolve in response to operational priorities and SDG implementation contexts. This empirical grounding supports the refinement of competency theories toward integrated, context-sensitive models that account for both visible, trainable skills and less visible, values-based attributes. Embedding bottom-up evidence into theory development can produce more adaptive frameworks that remain responsive to shifts in organizational mandates, technological change, and sustainability challenges, offering a stronger basis for aligning ESD policy, curriculum design, and workforce planning. Conclusion The study offers a comprehensive corpus-based analysis of STEM-related positions in the UN system with the purpose of exploring the relationship between competencies of STEM talents and the sustainable development. Drawing on the corpus of 33.68 billion words from 23190 recruitment postings in 2024, the study integrates corpus analysis and LDA topic modelling to assess competency demands across various agencies, locations, and position levels. Three major findings emerge. First, STEM positions constitute a significant part in all UN postings, indicating the need of technical expertise in sustainable development. Second, STEM positions are more located in Europe and North America with a demand of multilingual proficiency. Third, STEM positions demonstrate a higher possibility of reaching advanced professional levels than the non-STEM counterparts. Fourth, besides the core competencies of communication, professionalism, and teamwork, STEM positions emphasize data analysis, technological expertise, and systems thinking, which have not yet been included by existing UN competency frameworks. The findings carry the following implications for STEM education especially for sustainable education. On the one hand, the findings particularly underscore the need to align STEM curricula to real-world competencies in problem solving. On the other hand, a more updated competency framework is needed for STEM education for sustainable development, in which STEM talents are playing important roles. This research is not without limits. First, this research only collected data from 2024. This is because few websites are available or suitable for large data analyze, some of which already cleaned up recruited and outdated posts, making it difficult to trace past positions. Thus, only the selected website can be considered as qualified for the research. Secondly, the records on the website varies from year to year, so samples of other years except 2024 are too scarce to be studied, making it impossible to conduct diachronic research. Consequently, we cannot trace the variation of STEM proportion, world distribution, or competency in terms of time. Declarations Funding This research was financially supported by the Philosophy and Social Science Planning Project of Guangdong Province [grant number GD23CWY07], and the Guangdong Provincial Undergraduate Universities Teaching Quality and Teaching Reform Project [grant number C9248420]. References Aleksić, A, Nestić S, Huber M, & Ljepava N (2022) The Assessment of the Key Competences for Lifelong Learning—The Fuzzy Model Approach for Sustainable Education. Sustainability 14 (5): 2686.https://doi.org/https://doi.org/10.3390/su14052686 Ang, S, Ng K Y, & Rockstuhl T (2020) Cultural Intelligence. In R. J. Sternberg (Ed), The Cambridge Handbook of Intelligence . 2 edn, Cambridge University Press, pp 820-845. https://doi.org/10.1017/9781108770422.035 Aslam, S, Saleem A, Kennedy T J, Kumar T, Parveen K, Akram H, & Zhang B (2022) Identifying the research and trends in STEM education in Pakistan: A systematic literature review. Sage Open 12 (3): 21582440221118545.https://doi.org/10.1177/21582440221118545 Bartram, D (2005) The Great Eight Competencies: A Criterion-Centric Approach to Validation. Journal of Applied Psychology 90 (6): 1185-1203.https://doi.org/10.1037/0021-9010.90.6.1185 Breiner, J, Harkness S, Johnson C, & Koehler C (2012) What is STEM? A discussion about Conceptions of STEM in education and partnerships. School science and mathematics 112 .https://doi.org/10.1111/j.1949-8594.2011.00109.x Bybee, R W (2010) Advancing STEM education: A 2020 vision. Technology and engineering teacher 70 (1): 30 Cai, Z, Zhu J, & Tian S (2023) Research Progress of STEM Education Based on Visual Bibliometric Analysis. Sage Open 13 (3): 21582440231200157.https://doi.org/10.1177/21582440231200157 English Profile (2023) English Vocabulary Profile Online .https://www.englishprofile.org/wordlists/evp?start=15680. Gao, X, Li P, Shen J, & Sun H (2020) Reviewing assessment of student learning in interdisciplinary STEM education. International journal of STEM education 7 (1): 24.https://doi.org/10.1186/s40594-020-00225-4 Geagea, A, & MacCallum J (2020) Conversations with young people: Using a creative arts outreach programme to access, mobilise and activate capital to navigate to higher education. The Australian Educational Researcher 47 (5): 797-814.https://doi.org/10.1007/s13384-019-00364-w Heaslip, G, Vaillancourt A, Tatham P, Kovács G, Blackman D, & Henry M C (2019) Supply chain and logistics competencies in humanitarian aid. Disasters 43 (3): 686-708.https://doi.org/10.1111/disa.12361 Homberg, A, Klafke N, Glassen K, Loukanova S, & Mahler C (2020) Role competencies in interprofessional undergraduate education in complementary and integrative medicine: A Delphi study. Complementary Therapies in Medicine 54 : 102542.https://doi.org/10.1016/j.ctim.2020.102542 International Atomic Energy Agency (2025) Working at the IAEA .https://www.iaea.org/about/employment/working-at-the-iaea. Accessed August 28th Jiang, H, Zhang L, & Zhang W (2024) Influence of career awareness on STEM career interests: examining the roles of self-efficacy, outcome expectations, and gender. International Journal of STEM Education 11 (1): 22.https://doi.org/10.1186/s40594-024-00482-7 Kart, M, & Şimşek H (2024) Defining competencies in curriculum and instruction and developing a new competency model. Humanities and Social Sciences Communications 11 (1): 1496.https://doi.org/10.1057/s41599-024-03917-2 Kleinschmit, A J, Rosenwald A, Ryder E F, Donovan S, Murdoch B, Grandgenett N F, Pauley M, Triplett E, Tapprich W, & Morgan W (2023) Accelerating STEM education reform: linked communities of practice promote creation of open educational resources and sustainable professional development. International Journal of STEM Education 10 (1): 16.https://doi.org/10.1186/s40594-023-00405-y Kurz, R, & Bartram D (2008) Competency and Individual Performance: Modelling the World of Work. In., pp 227-255. https://doi.org/10.1002/9780470696736.ch10 Lin, K-Y, Yeh Y-F, Hsu Y-S, Wu J-Y, Yang K-L, & Wu H-K (2023) STEM education goals in the twenty-first century: Teachers’ perceptions and experiences. International Journal of Technology and Design Education 33 (2): 479-496.https://doi.org/10.1007/s10798-022-09737-2 Liu, Y, Zhao H, & Chen Y (2024) A Data-driven Analysis of STEM-related Positions in UN International Organizations. Journal of Humanities, Arts and Social Science Lozano, R, Merrill M Y, Sammalisto K, Ceulemans K, & Lozano F J (2017) Connecting competences and pedagogical approaches for sustainable development in higher education: A literature review and framework proposal. Sustainability 9 (10): 1889 MacKay, M, Ford C, Grant L E, Papadopoulos A, & McWhirter J E (2024) Developing competencies in public health: a scoping review of the literature on developing competency frameworks and student and workforce development. FRONTIERS IN PUBLIC HEALTH 12 Article 1332412.https://doi.org/10.3389/fpubh.2024.1332412 Pérez-Foguet, A, Lazzarini B, Giné R, Velo E, Boni A, Sierra M, Zolezzi G, & Trimingham R (2018) Promoting sustainable human development in engineering: Assessment of online courses within continuing professional development strategies. Journal of Cleaner Production 172 : 4286-4302.https://doi.org/10.1016/j.jclepro.2017.06.244 Quendler, E, & Lamb M (2016) Learning as a lifelong process - Meeting the challenges of the changing employability landscape: Competences, skills and knowledge for sustainable development. International Journal of Continuing Engineering Education and Life-Long Learning 26 : 273.https://doi.org/10.1504/IJCEELL.2016.078447 Řehůřek, R, & Sojka P (2010) Software Framework for Topic Modelling with Large Corpora https://doi.org/10.13140/2.1.2393.1847 Shin, E, Yim S, & Koh A-r (2025) Comparison of consumer perceptions of sustainable and ethical fashions pre- and post-COVID-19 using LDA topic modeling. Humanities and Social Sciences Communications 12 (1): 226.https://doi.org/10.1057/s41599-025-04525-4 Spencer, L M, & Spencer P S M (1993) Competence at Work: Models for Superior Performance .John Wiley & Sons The United Nations High Commissioner for Refugees (2024) UNHCR Interview Questions: Core Values and Competencies .https://undpjobs.net/unhcr-interview-questions-core-values-and-competencies. Accessed Augest 16th Tse, L (1996) Language brokering in linguistic minority communities: The case of Chinese-and Vietnamese-American students. Bilingual research journal 20 (3-4): 485-498 U.S. Bureau of Labor Statistics (2025) Employment in STEM occupations .https://www.bls.gov/emp/tables/stem-employment.htm. U.S. Immigration and Customs Enforcement (2024) DHS Announces Updated DHS STEM Designated Degree Program List .https://www.ice.gov/news/releases/dhs-announces-updated-dhs-stem-designated-degree-program-list. UN Office of Human Resources Management (2009) United Nations competencies for the future .https://digitallibrary.un.org/record/699801?ln=en&v=pdf. Accessed April 26th Un Women (2023) Application and recruitment process .https://www.unwomen.org/en/about-us/employment/application-process. Accessed August 16th UNDP (n.d.) UNDP Competency Framework https://www.undp.org/careers/what-we-look-for. Accessed August 17th UNESCO Digital Library(2016) UNESCO https://unesdoc.unesco.org/ark:/48223/pf0000245056 August 17th UNICEF (n.d.) Get prepared .https://www.unicef.org/careers/unicef-core-values. Accessed August 29th UNIDO (2022) UNIDO Competencies .https://dl.hrmacy.ir/wp-content/uploads/2022/07/29190401/UNIDO-2.pdf. Accessed August 29th Uralovich, K, Tulakov U, Kubayevich K, Sapaev I, Saylaubaevna S, Beknazarova Z, & Khurramov A (2023) A primary factor in sustainable development and environmental sustainability is environmental education. 11 .https://doi.org/10.22124/CJES.2023.7104 Wiek, A, Keeler L, & Redman C (2011) Key Competencies in Sustainability: a Reference Framework for Academic Program Development. Sustainability science 6 : 203-218.https://doi.org/10.1007/s11625-011-0132-6 Wiek, A, & Redman A (2022) What Do Key Competencies in Sustainability Offer and How to Use Them. In., pp 27-34. https://doi.org/10.1007/978-3-030-91055-6_4 Yu, T Z, Shang W W, Liu S X, & Zhu J B (2022) How to Assess Generic Competencies: From Sustainable Development Needs among Engineering Graduates in Industry. Sustainability 14 (15) Article 9270.https://doi.org/10.3390/su14159270 Zhan, Z, Li Y, Mei H, & Lyu S (2023) Key competencies acquired from STEM education: gender-differentiated parental expectations. Humanities and Social Sciences Communications 10 (1): 464.https://doi.org/10.1057/s41599-023-01946-x Zhang, X, Liu Y, & Lin J (2024) Dilemmas and Pathways of Education of Humanities and Social Sciences from an International Comparative Perspective. China Higher Education Research (11): 77-83.https://doi.org/10.16298/j.cnki.1004-3667.2024.11.11 Additional Declarations No competing interests reported. Supplementary Files AllPostingstxt.zip Appendixs.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Feb, 2026 Editor invited by journal 05 Nov, 2025 Editor assigned by journal 21 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 30 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"Xifei","middleName":"","lastName":"Liu","suffix":""},{"id":590714828,"identity":"718fab0e-839c-48a2-8222-dbc2c926f870","order_by":2,"name":"Yanyan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBAC9nYQWQHl8RCjhecwiDwDxGwkaWFsI0kLM/Mzad55dfIG9xsYH7xtY5A3J6yFzUyad9thww3HGJgN57YxGO5sIKDFnpmHDajlQILBMQYgo40hweAAQVtAWubUgbSw/yZBSwMz2BZmIrWwGVvOOXbYcOaxxGbJOeckDDcQ1MLe/PDGm5o6eb7Dhw9+eFNmI0/QFiBgkYJEB2MDkJAgrB4ImD/+IErdKBgFo2AUjFgAALElNVMv5bqpAAAAAElFTkSuQmCC","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-30 10:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7494569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7494569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102742929,"identity":"229b4f30-f2a0-40b0-b005-1e801adaa2e1","added_by":"auto","created_at":"2026-02-16 08:11:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":414200,"visible":true,"origin":"","legend":"\u003cp\u003eWorld distribution of UN recruitment.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/a08f64e4271b9ba7f56e7b95.png"},{"id":102743040,"identity":"fa5a759a-18b4-4e79-998e-0a0bbb1d169e","added_by":"auto","created_at":"2026-02-16 08:11:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50249,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of STEM and non-STEM roles across various levels of positions.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/29de92218af99b837828eae0.png"},{"id":102742939,"identity":"114648b9-8604-4768-8bdd-becfb13fd6a6","added_by":"auto","created_at":"2026-02-16 08:11:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179097,"visible":true,"origin":"","legend":"\u003cp\u003eLDA model performance by topic number of STEM and non-STEM positions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/fb03ece07a1f725d30f4095e.png"},{"id":102743067,"identity":"cdaaf03c-ab38-4768-afe6-84e5da7da1bf","added_by":"auto","created_at":"2026-02-16 08:11:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191862,"visible":true,"origin":"","legend":"\u003cp\u003eIntertopic distance map for LDA topic modeling on STEM posts.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/c1a56178b80d8f4702d8add6.png"},{"id":102743087,"identity":"4f8486f7-f46c-4423-92d4-fdaf6f7c0105","added_by":"auto","created_at":"2026-02-16 08:11:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":180933,"visible":true,"origin":"","legend":"\u003cp\u003eIntertopic distance map for LDA topic modeling on non-STEM posts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/b0dd50fb0a16d63f297d0947.png"},{"id":102743099,"identity":"95c1d5cd-bc25-45ed-9ef8-c4871b1e2219","added_by":"auto","created_at":"2026-02-16 08:12:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/c11826cf-d430-40ff-8336-b7db5bbb968a.pdf"},{"id":102743069,"identity":"e2260b96-f099-4e7f-9a94-d2a635f3eae3","added_by":"auto","created_at":"2026-02-16 08:11:51","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":82914925,"visible":true,"origin":"","legend":"","description":"","filename":"AllPostingstxt.zip","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/ac8d8b2ad2af1bb2b0df4a68.zip"},{"id":102743068,"identity":"01722992-db25-4d8a-b3e1-251419224a8d","added_by":"auto","created_at":"2026-02-16 08:11:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24607,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixs.docx","url":"https://assets-eu.researchsquare.com/files/rs-7494569/v1/d54d6d7dad45ef721a2d53c9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"STEM in Sustainable Development: A Competency-Based Corpus Analysis of UN Job Postings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn an era where the UN is tasked with addressing increasingly complex global challenges, from climate change and humanitarian crises to digital transformation, the alignment of workforce competencies with the demands of sustainable development has become a strategic priority. While existing scholarship has examined competency frameworks in broad terms, few studies have empirically mapped the actual skill requirements of UN positions to established models such as the United Nations Competencies for the Future (UNCF) and the 2030 Agenda for Sustainable Development(UN Office of Human Resources Management, 2009). This gap is particularly significant in distinguishing between STEM and non-STEM roles, as a clearer understanding of their distinct competency demands is essential for developing education and training pathways for Education for Sustainable Development (Kleinschmit et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy leveraging large-scale corpus analysis and Latent Dirichlet Allocation (LDA) topic modeling of over 33 million words from UN job postings, this study provides an evidence-based understanding of how competencies are operationalized across job categories. The findings offer critical insights for education policy, curriculum design, and global workforce development, ensuring that talent pipelines are responsive to the evolving demands of sustainable development.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003ch2\u003e\u003cstrong\u003ePrevious competency frameworks for sustainable development\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCompetency frameworks provide structured guidelines for defining the skills, knowledge, and attributes necessary for effective job performance. Research on competency frameworks have generally two main strands, i.e., the development and improvement of competency frameworks, and education to meet the needs of the competency frameworks (MacKay et al., 2024). The most classic and widely applicable generic competency framework is the Great Eight competency model for various jobs and organizations (Kurz \u0026amp; Bartram(2008); Bartram,(2005). Competency model is also hierarchical with the typical framework proposed by Organisation for Economic Co-operation and Development (OECD), including eight competencies at three levels of Performing, Inspiring, and Leading (Organisation for Economic Co-operation and Development, 2024). The OECD model is notable for its behaviorally anchored descriptions and multi-level progression, which make it adaptable across sectors, including international organizations. However, while its emphasis on innovation, collaboration, and ethics resonates strongly with the goals of sustainable development, the framework is primarily designed for intergovernmental policy contexts and may not fully target for sustainability in workplace.\u003c/p\u003e\n\u003cp\u003eAmong workplace-specific competency models in the field of sustainable development, the UNCF framework is designed to “create an organizational culture and environment that enables staff to contribute to their maximum potential”. It outlines three core values, eight core competencies, and six managerial competencies (Appendix A). Based on UNCF, UN agencies have published more specific competency frameworks, including UNHCR, UN-women, UNESCO, UNIDO, UNICEF, UNDP, and International Atomic Energy Agency (see Appendix A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo integrate multiple UN agency frameworks into a coherent analytical base, the present study synthesized recurring competencies into an adapted UNCF-aligned taxonomy (see Table 1). This taxonomy preserves the UNCF’s three-tier structure—core values, core competencies, and managerial competencies—while consolidating overlapping elements from nine major UN and related frameworks. Each competency cluster was assigned a coverage score, reflecting the number of frameworks in which it appears, and linked to a primary Sustainable Development Goal (SDG) to illustrate its relevance to sustainable development objectives. This synthesis highlights the competencies most consistently prioritized across the UN system and provides a benchmark for comparing corpus-derived recruitment demands with established institutional expectations.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Competency taxonomy (coverage-weighted) with SDG linkage.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSynthesized competency cluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage (of 9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary SDG link\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntegrity / Ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRespect for diversity / Inclusion (incl. cultural \u0026amp; gender sensitivity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 10, SDG 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfessionalism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOrganizational commitment / Care / Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore competencies – Collaboration \u0026amp; influence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTeamwork / Collaboration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePartnership / Client orientation / Stakeholder engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore competencies – Delivery \u0026amp; results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eResults focus / Accountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 16, SDG 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePlanning \u0026amp; organizing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore competencies – Learning \u0026amp; innovation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous learning / Knowledge sharing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCreativity / Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdaptability / Resilience / Managing ambiguity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore competencies – Technical \u0026amp; analytical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnalytical thinking / Problem solving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnological awareness / Scientific credibility / Information management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eManagerial competencies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeadership (incl. leading \u0026amp; supervising)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEmpowering others / People management / Capability building\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVision / Strategic thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eManaging performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDecision-making / Judgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChange management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9, SDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePartnership building (managerial)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrust building\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMoreover, the frameworks mentioned above have not been fully tested for validity in real UN recruitment, leaving a gap in understanding how well these frameworks align with actual competency demands in practice. Actual competency demands are mostly examined through data-driven approach in previous research of industry-specific hiring trends (Geagea \u0026amp; MacCallum, 2020; Homberg et al., 2020; Aleksić et al., 2022; Kart \u0026amp; Şimşek, 2024)\u0026nbsp;.\u0026nbsp;In particular, as science- and technology-intensive fields of sustainable development, STEM-related roles warrant urgent investigation, given their central role in addressing complex global issues. This gap highlights the need for evidence-based approaches, such as corpus analysis, to investigate how competencies are represented in UN job postings and whether these align with evolving organizational priorities. This alignment is critical for both policy-makers and education practitioners seeking to prepare talent for careers concerning sustainable development\u0026nbsp;(Quendler \u0026amp; Lamb, 2016).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eIntegrating sustainable development and STEM education\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAs an essential component of sustainable development, STEM education combines scientific inquiry, technological application, engineering design, and mathematical reasoning to equip learners with the capacity to tackle complex, real-world challenges through interdisciplinary and solutions-oriented approaches (Aslam et al., 2022; Cai et al., 2023). Recent scholarship links STEM’s problem-solving orientation to addressing global sustainability challenges, however, relatively few studies examine whether STEM programs develop the competencies most relevant to professional contexts, particularly those requiring interdisciplinary collaboration (Gao et al., 2020).\u003c/p\u003e\n\u003cp\u003eEducation for sustainable development emphasizes the integration of sustainability principles into curricula, particularly in science and engineering disciplines (Uralovich et al., 2023). Global policy milestones, such as Our Common Future (1987), Agenda 21 (1992), and the United Nations Sustainable Development Goals (2015), have underscored the need for scientific and technological expertise to address environmental, social, and economic challenges. In this context, STEM graduates are increasingly expected to apply their expertise to advancing sustainable development and addressing its environmental, social, and economic dimensions (Lozano et al., 2017).\u003c/p\u003e\n\u003cp\u003eRecognizing the importance of STEM expertise, the UN and its agencies seek professionals capable of addressing complex global challenges (Pérez-Foguet et al., 2018). Existing research on education for sustainable development has highlighted the importance of integrating sustainability principles into science and engineering curricula (Lin et al., 2023). Most studies on sustainability-related careers emphasize broad skills such as adaptability and cross-sector collaboration (Spencer \u0026amp; Spencer, 1993; Lozano et al., 2017). Field-specific examples include Heaslip et al.\u0026nbsp;(2019), which developed a T-shaped humanitarian logistics competency framework, and Shin et al. (2025), which applied LDA topic modeling to examine sustainable ethics discourse.\u003c/p\u003e\n\u003cp\u003eHowever, much of the literature on STEM in education for sustainable development remains theoretical, with limited empirical validation against actual STEM-related position needs (Bybee, 2010; Breiner et al., 2012). While conceptual competency frameworks describe desired competencies, they do not necessarily capture how recruitment prioritize these competencies in practice. This is particularly salient for international organizations such as UN, where STEM expertise is central to achieving SDGs. This study addresses this gap by examining the competencies explicitly sought in UN job postings and assessing their alignment with established frameworks for sustainability-focused STEM careers.\u003c/p\u003e\n\u003cp\u003eBased upon the literature review, the study aims to address the following research gaps. First, although STEM is widely acknowledged as essential to sustainable development, few studies connect STEM education and the recruitment practices of international organizations such as the UN. In particular, little is known about how educational preparation aligns with the specific competency requirements for sustainable development. Second, while competency frameworks are widely used in UN systems, they are more likely to be top-down models. Such measures, including expert consultations and survey-based evaluations, are likely to fail to capture how international organizations in UN system actually prioritize competencies in recruitment . Accordingly, the research seeks respond tothese gaps by examining the following research questions (RQs).\u003c/p\u003e\n\u003cp\u003eRQ1: What is the distribution of STEM and non-STEM roles in UN system in terms of job proportions, locations, language requirements, and position levels?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRQ2: How are competencies required for STEM roles in UN system, as they emerge from the corpus and compared with UNCF?\u003c/p\u003e\n\u003cp\u003eDrawing on a 33.68-million-word corpus of UN job postings, this study identifies the competencies most frequently required for STEM-related sustainability roles and compares them with the UNCF framework. The findings will (1) provide empirical evidence on STEM competency demands in the UN system, (2) inform the refinement of competency frameworks to better reflect sustainability-focused roles, and (3) offer actionable insights for policymakers and educators to align training with workforce needs in global governance.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003eResearch design\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study aims to provide an empirical, data-driven evaluation of competency expectations in STEM-related sustainability careers. Instead of relying on static competency models, this research systematically quantifies which competencies are most frequently required across different roles and how these competencies align with the UN competency frameworks.\u003c/p\u003e\n\u003cp\u003eTo address RQ1, a descriptive analysis is conducted to quantify the prevalence of STEM-related job postings across UN agencies. The proportions, locations, language requirements, and position levels of STEM positions are calculated and visualized by Python 3.13. This step establishes a baseline understanding of STEM representation in the UN system, providing essential context for the competency analysis in RQ2.\u003c/p\u003e\n\u003cp\u003eTo tackle RQ2, the targeted sections describing competency requirements were extracted from the whole texts, and then an LDA model is used to identify topics of competencies. This method enables a comparison of the LDA outcomes with the UNCF framework, which includes key competencies such as Professionalism, Communication, Teamwork, Leadership, and Innovation, and identifies the gap between competencies required in real recruitment and the counterpart in existing frameworks.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData collection and classification\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study collected recruitment posts via Python 3.13 from uncareer.net, a widely used aggregator of UN vacancies. Each posting contains structured metadata, including \u0026ldquo;Date,\u0026rdquo; \u0026ldquo;Organization\u0026rdquo;, \u0026ldquo;Country\u0026rdquo;, \u0026ldquo;City\u0026rdquo;, \u0026ldquo;Post Level\u0026rdquo;, and the full job description. To ensure corpus validity, postings were included only if they provided complete metadata and a full English text version. Duplicates were removed, and non-recruitment content (such as website headers and footers) was excluded. In total, 23,190 postings from 2024 were collected, forming a 33.68-million-word corpus. This procedure follows corpus-linguistics principles of authenticity, representativeness, and systematic sampling (Baker, 2006; McEnery \u0026amp; Hardie, 2012), thereby ensuring that the corpus accurately reflects UN recruitment discourse. To distinguish between STEM posts and non-STEM posts, the study utilized the U.S. Department of Homeland Security STEM Designated Degree Program (DHS) List (2024) as the keywords for Python selection. This list, widely recognized in educational and labor market research, provides a standardized and periodically updated classification of STEM disciplines, thereby ensuring methodological transparency and cross-study comparability (U.S. Immigration and Customs Enforcement, 2024). Keywords from this list were refined to improve retrieval accuracy, including the removal of non-specific terms and minor linguistic adjustments (see Appendix B for full keyword processing steps).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData preprocessing\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAs UN posts are composed of English and non-English versions, we\u0026rsquo;ve built a word corpus containing all potential words in the study, ranging from A1 to C2 position levels. To collect the words, we\u0026rsquo;ve accessed all words and phrases in an online CFER English vocabulary profile(English Profile, 2023), as well as Oxford 3000, Oxford 5000, and Longman Communication 3000.\u003c/p\u003e\n\u003cp\u003eThe English text data underwent standard preprocessing, i.e., (1) removal of URLs and stopwords, lowercasing, (2) tokenization using NLTK, part-of-speech tagging, and retention of nouns, adjectives, and adverbs. Bi-grams were identified using the Phrases function in Gensim, the open-source Python library for topic modeling (Řehůřek \u0026amp; Sojka, 2010).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTopic modeling\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePreprocessed text was converted into numerical vectors using Gensim\u0026rsquo;s corpora module (Řehůřek \u0026amp; Sojka, 2010). LDA, a probabilistic method for uncovering latent thematic structures in text (Blei et al., 2003), was applied to identify clusters of related competencies, with recent advances further improving efficiency and coherence (Song et al., 2025; Sarker et al., 2025). To remove vocabularies with minimal impact from the LDA model, this research only chose 90% percentile used to filter out the top 10% of words with the highest weight, which are usually the core words of the topic.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eRQ1: Distributions of STEM and non-STEM roles\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003eProportion of STEM vs. Non‑STEM Posts\u003c/h3\u003e\n\u003cp\u003eOf the 22174 postings with sufficient classification information, 16142 (72.8%) were STEM roles and 6032 (27.2%) were non-STEM. This significant STEM share emphasize the scientific and technical orientation of the UN workforce, reflecting UN\u0026rsquo;s reliance on STEM expertise in sustainable development.\u003c/p\u003e\n\u003cp\u003eSTEM-related roles covered a wide range of disciplines, with the largest concentrations in planetary sciences (\u003cem\u003en\u003c/em\u003e=276), information and communications technology (\u003cem\u003en\u003c/em\u003e=212), computer science (\u003cem\u003en\u003c/em\u003e=206), agroecology/agriculture (\u003cem\u003en\u003c/em\u003e=162), and transportation engineering (\u003cem\u003en\u003c/em\u003e=157). The range of disciplines reflects the UN\u0026rsquo;s multidisciplinary approach to achieving the SDGs, where expertise in environmental sciences supports climate action and resource management, digital technologies enable data-driven decision-making and innovation, and infrastructure development underpins resilient cities, transportation networks, and essential services in both humanitarian and development contexts. A full list of educational requirements for STEM positions is provided in Appendix C.\u003c/p\u003e\n\u003ch3\u003eLocations of STEM and Non-STEM Posts\u003c/h3\u003e\n\u003cp\u003eThe geographic distribution of UN recruitment reveals distinct patterns between STEM and non-STEM roles are shown in Table 2 and visualised in Fig. 1. For STEM positions, the highest concentrations were in Switzerland (10.82%) and the United States (10.69%), reflecting the presence of major UN headquarters and specialized agencies in Geneva and New York. Other high-ranking countries for STEM recruitment included France, Sudan, Nigeria, Kenya, Ukraine, Ethiopia, the United Kingdom, and India.\u003c/p\u003e\n\u003cp\u003eNon-STEM positions showed a different profile, with Germany (9.80%) at the top, followed by the United States, Switzerland, Colombia, and France. Several Latin American countries (e.g., Colombia, Mexico, Ecuador, Spain) appeared in the top 10 for non-STEM recruitment, suggesting a stronger regional spread for administrative and programmatic roles. This result is consistent with how the UN system allocates work and offices. Germany, the U.S., and Switzerland host major UN headquarters or specialized agencies (e.g., UN Bonn campus in Germany, UN Secretariat and agencies in New York, WHO in Geneva).\u003c/p\u003e\n\u003cp\u003eOverall, STEM recruitment exhibited a dual geographic concentration. The first cluster comprised high-income administrative and policy hubs, such as Switzerland, the United States, France, and the United Kingdom. These countries typically serve as centres for global technical coordination and strategic planning. The second cluster encompassed field operation centres in developing or crisis-affected regions, including Sudan, Nigeria, Kenya, Ethiopia, Ukraine, and India, where engineering, health, infrastructure, and environmental expertise are essential to implementing the UN\u0026rsquo;s sustainable development agenda and humanitarian operations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Job\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eocations of STEM and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003eon-STEM\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eosts\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-STEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSudan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eColombia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDemocratic Republic of Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEcuador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eLanguage Requirements of STEM and Non-STEM Posts\u003c/h3\u003e\n\u003cp\u003eTable 3 shows that English dominates recruitment across both STEM and non-STEM roles, appearing in over half of all postings (55.41% overall). French is the second most requested language (19.10% overall), with slightly higher demand in STEM positions (19.76%) than in non-STEM (14.55%), followed by Spanish, Arabic, Russian, and Chinese. The total counts exceed the number of posts because many vacancies require more than one official UN language. This is especially pronounced in STEM roles, where the combined ratio of language requirements (136.25%) far surpasses that of non-STEM roles (52.65%), indicating stronger multilingual expectations in technical and field-based functions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Languages required in STEM and non-STEM recruitment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRanking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage Required\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-STEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e13945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e55.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e12059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e54.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e59.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFrench\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e14.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSpanish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eArabic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e8.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e8.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e9.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eRussian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAll Languages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e21993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAll Posts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e22174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e113.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e16142\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e136.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e52.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese patterns reflect the UN\u0026rsquo;s operational footprint, particularly in Francophone Africa and Geneva- and Paris-based agencies where French serves alongside English as a working language. The higher multilingual demand for STEM posts suggests that technical experts often work in contexts requiring close engagement with local stakeholders and regional offices. In the synthesis competency framework, such language skills strengthen \u0026ldquo;Collaboration \u0026amp; Influence\u0026rdquo; competencies, especially communication and stakeholder engagement, and reinforce \u0026ldquo;Respect for Diversity/Inclusion\u0026rdquo; as a core value, i.e., positioning multilingualism as a strategic enabler of effective, culturally responsive UN operations. This shows a feature of \u0026ldquo;facilitating communication between linguistically and culturally different parties\u0026rdquo;(Tse, 1996).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003ePost Levels of STEM and Non-STEM roles\u003c/h3\u003e\n\u003cp\u003eThe distribution of STEM and non-STEM positions varies across professional levels (Fig. 2 and Table 4). Percentages reflect the relative share within each category. Higher-level positions (General Support to Director/Top Executive) are dominated by STEM roles, reflecting increasing demands for synthesis-framework competencies in the Technical \u0026amp; Analytical, Delivery \u0026amp; Results, and Managerial clusters, linked primarily to SDG 9 (Industry, Innovation and Infrastructure) and SDG 17 (Partnerships for the Goals). In contrast, internships are more accessible to non-STEM applicants, drawing on Core Values and Collaboration \u0026amp; Influence competencies that are broadly transferable across disciplines.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Numbers and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eroportions of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eost\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eevels between STEM and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003eon-STEM\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eosts\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 237px;\"\u003e\n \u003cp\u003eTypes of Positions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNon-STEM\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eInternship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e575 (44.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e711 (55.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eGeneral Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e281 (71.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e111 (28.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eEntry Professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e388 (67.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e183 (32.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eMid-level Professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e352 (73.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e126 (26.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eChief and Senior Professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e252 (77.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e75 (22.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eDirector and Top Executive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e463 (70.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e198 (29.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eRQ2: Competencies emphasized for STEM and non-STEM positions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo find the optimized number of topics (\u0026ldquo;K\u0026rdquo;), we\u0026rsquo;ve explored the complexity and credibility of different types of posts, iterating 100 times. We finally chose 18 topics for STEM posts and 8 topics for Non-STEM posts. As shown in Fig. 3, the number of topics was determined by balancing coherence (higher is better) and perplexity (lower is better). For STEM posts, 18 topics achieved relatively high coherence with stable perplexity, while for Non-STEM posts, 8 topics provided the best balance, thus avoiding overfitting while maintaining interpretability. In the LDA model building, the data was iterated 300 times. These findings are interpreted in light of the UNCF framework and the 2030 Agenda for Sustainable Development.\u003c/p\u003e\n\u003ch3\u003eLDA topic modeling for competencies required by STEM posts\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe LDA analysis of STEM job postings reveals a well-differentiated set of competency demands, reflecting the multifaceted nature of technical work in the UN system. The 18 topics extracted range from highly specialised areas such as climate change mitigation, engineering practice, and cybersecurity to cross-cutting skills in programme management, legal expertise, multilingual communication, and respect for diversity. The prevalence of both domain-specific and transferable competencies underscores that STEM roles in the UN require a blend of advanced technical knowledge, sector-specific experience, and the ability to operate effectively in multicultural, interdisciplinary contexts aligned with the UNCF framework and the 2030 Agenda.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTopic 1 (Agriculture and Food Security), constituting approximately 4.08% of the analyzed corpus, revolves around \u0026ldquo;security,\u0026rdquo; \u0026ldquo;agriculture\u0026rdquo;, \u0026ldquo;humanitarian\u0026rdquo;, and \u0026ldquo;nutrition\u0026rdquo;. This topic is highly related to specific majors on the DHS List and requires applicants\u0026rsquo; identification of humanitarian principles to conform to the United Nations working environment.\u003c/p\u003e\n\u003cp\u003eRepresenting 7.03% of the corpus, Topic2 (Financial Analysis) focuses on financial analysis and accounting skills. Dominant terms like \u0026ldquo;financial\u0026rdquo;, \u0026ldquo;accounting\u0026rdquo;, and \u0026ldquo;business\u0026rdquo; denote competencies in budgeting, auditing, and financial reporting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt 4.61% of the corpus, Topic3 (Program Management) emphasizes terms like \u0026ldquo;program\u0026rdquo;, \u0026ldquo;fluency\u0026rdquo;, and \u0026ldquo;communication\u0026rdquo;, together with \u0026ldquo;leadership\u0026rdquo; \u0026ldquo;master\u0026rdquo;, demanding high education level in program management and professional competencies for communication.\u003c/p\u003e\n\u003cp\u003eConstituting 4.75% of the corpus, Topic4 (Climate Change and Environmental Protection) targets competencies in ecological governance. Terms like \u0026ldquo;climate\u0026rdquo;, \u0026ldquo;environmental\u0026rdquo;, and \u0026ldquo;sustainable\u0026rdquo; highlight skills in carbon mitigation and green policy drafting, which is related to specific majors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt 3.50% of the corpus, Topic5 (Cybersecurity and Digital Infrastructure Management) prioritizes technical and operational competencies in IT systems with terms \u0026ldquo;security\u0026rdquo;, \u0026ldquo;network\u0026rdquo;, \u0026ldquo;science\u0026rdquo; and \u0026ldquo;web\u0026rdquo;. This means that candidates should handle with cyber risk and IT system administration.\u003c/p\u003e\n\u003cp\u003eRepresenting 3.61% of the corpus, Topic6 (Economic Engineering) merges financial and technical competencies. With terms stressing financial profession including \u0026ldquo;economics\u0026rdquo;, \u0026ldquo;finance\u0026rdquo;, and terms stressing education level and management like \u0026ldquo;administration\u0026rdquo; \u0026ldquo;master\u0026rdquo; \u0026ldquo;professional\u0026rdquo;, this topic has strong indication of higher managerial level of STEM positions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTopic 7 (Business and Communication) represents 6.25% of the corpus, with components like \u0026ldquo;business\u0026rdquo;, \u0026ldquo;communication\u0026rdquo; \u0026ldquo;economics\u0026rdquo;, and \u0026ldquo;leadership\u0026rdquo;. This means candidates should understand core business concepts and demonstrate leadership in group tasks.\u003c/p\u003e\n\u003cp\u003eTopic 8 (Respect for Diversity), accounting for 5.05% of the corpus, emphasizes ethical governance and intercultural adaptability. This topic has unique key words including \u0026ldquo;diversity\u0026rdquo;, \u0026ldquo;commitment\u0026rdquo;, \u0026ldquo;sensitivity\u0026rdquo;, \u0026ldquo;respect\u0026rdquo;, \u0026ldquo;willingness\u0026rdquo;, and \u0026ldquo;accountability\u0026rdquo;, setting up moral codes in accordance with the United Nations core values.\u003c/p\u003e\n\u003cp\u003eRepresenting 6.13% of the corpus, Topic 9 (Humanitarian Coordination and Crisis Response) has dominant terms like \u0026ldquo;humanitarian\u0026rdquo; and \u0026ldquo;coordination\u0026rdquo; emphasize competencies in disaster preparedness and interagency collaboration. To achieve this, \u0026ldquo;language\u0026rdquo;, \u0026ldquo;fluency\u0026rdquo; and \u0026ldquo;response\u0026rdquo; are required as basic skills.\u003c/p\u003e\n\u003cp\u003eTopic 10 (Digital Media Proficiency and Technical Communication), takes up 4.93% of the corpus, \u0026ldquo;media\u0026rdquo;, \u0026ldquo;software\u0026rdquo;, \u0026ldquo;digital\u0026rdquo; and \u0026ldquo;design\u0026rdquo; reflect digital literacy in multimedia production. Employees for such positions indicate expertise in online platforms and \u0026ldquo;journalism\u0026rdquo; to enhance the United Nations influence in the digital era and construct positive images for their working agencies.\u003c/p\u003e\n\u003cp\u003eConstituting 9.25% of the corpus, Topic 11 (Dada Processing and Statistical Analysis) is composed of \u0026ldquo;data\u0026rdquo;, \u0026ldquo;analysis\u0026rdquo;, \u0026ldquo;software\u0026rdquo;, \u0026ldquo;collection\u0026rdquo;, which means a good command of databases, data warehousing, and data governance.\u003c/p\u003e\n\u003cp\u003eTopic 12 is Basic Office skills, taking up 6.31% of the corpus. Employees are able to use \u0026ldquo;Excel\u0026rdquo;, and fluent in both \u0026ldquo;oral\u0026rdquo; and \u0026ldquo;writing\u0026rdquo; working language to have \u0026ldquo;communication\u0026rdquo;, as well as withstanding \u0026ldquo;pressure\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eRepresenting 6.05% of the corpus, Topic 13 (Business Strategy and Administration) combines strategic planning and relationship management. It has key words including \u0026ldquo;business\u0026rdquo;, \u0026ldquo;administration\u0026rdquo;, \u0026ldquo;fluency\u0026rdquo;, and \u0026ldquo;communication\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eTopic 14, Technical Background, representing 5.71% of the corpus, focuses on skill transfer and institutional strengthening. Key terms consist of \u0026ldquo;technical\u0026rdquo;, \u0026ldquo;evaluation\u0026rdquo;, \u0026ldquo;capacity\u0026rdquo; and \u0026ldquo;training\u0026rdquo;, which emphasizes receiving education in science and technology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTopic 15 (Engineering Background) constitutes 6.63% of the corpus. This topic targets engineering excellence with \u0026ldquo;engineering, \u0026ldquo;application\u0026rdquo;, \u0026ldquo;eligible\u0026rdquo;, \u0026ldquo;design\u0026rdquo; and \u0026ldquo;software\u0026rdquo;. This dimension is related to candidates\u0026rsquo; ability\u0026nbsp;of\u0026nbsp;engineering\u0026nbsp;practice.\u003c/p\u003e\n\u003cp\u003eAt 8.73% of the corpus, Topic 16 (Legal Language Competency) merges \u0026ldquo;law\u0026rdquo; and \u0026ldquo;legal\u0026rdquo; in legal expertise, and \u0026ldquo;language\u0026rdquo; and \u0026ldquo;professional\u0026rdquo; in language proficiency. This indicates the applicants should be proficient in working language and mastering knowledge in required legal field.\u003c/p\u003e\n\u003cp\u003eTopic 17 (Technical Language Proficiency), representing 4.27% of the corpus, addresses hands-on technical roles. Terms such as \u0026ldquo;language\u0026rdquo;, \u0026ldquo;professional\u0026rdquo;, \u0026ldquo;completion\u0026rdquo;, and \u0026ldquo;secondary\u0026rdquo; target candidates in skilled trades or technical roles.\u003c/p\u003e\n\u003cp\u003eTopic 18 (Community-Based Technical Governance), at 3.12% of the corpus, this topic centers on green initiatives and participatory governance with key terms \u0026ldquo;community\u0026rdquo;, \u0026ldquo;energy\u0026rdquo;, \u0026ldquo;nutrition\u0026rdquo; and \u0026ldquo;manager\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eAn overview of all 18 topics derived from the LDA modeling of STEM job postings, including their proportions and key terms, is provided in Table 6.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 6 Results of LDA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eopic modeling for STEM posts\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 362px;\"\u003e\n \u003cp\u003eContent (Weight)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic1: Agriculture and Food Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003esecurity (0.149) + agriculture (0.038) + humanitarian (0.035) + communication (0.027) + legal (0.023) + nutrition (0.021) + fluency (0.021) + leadership (0.019) + law (0.018) + professional (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic2:\u003c/p\u003e\n \u003cp\u003eFinancial Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003efinancial (0.126) + finance (0.078) + accounting (0.077) + business (0.033) + administration (0.031) + professional (0.029) + analysis (0.027) + communication (0.026) + oral (0.018) + language (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic3:\u003c/p\u003e\n \u003cp\u003eProgram Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003eprogramme (0.055) + fluency (0.040) + communication (0.038) + technical (0.032) + expertise (0.030) + leadership (0.029) + master (0.028) + population (0.026) + professional (0.023) + prevention (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic4:\u003c/p\u003e\n \u003cp\u003eClimate Change and Environmental Protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003eclimate (0.101) + environmental (0.068) + communication (0.036) + science (0.030) + agricultural (0.026) + sustainable (0.023) + environment (0.022) + oral (0.021) + capacity (0.020) + master (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic5:\u003c/p\u003e\n \u003cp\u003eCybersecurity and Digital Infrastructure Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003esecurity (0.056) + relations (0.040) + network (0.030) + communication (0.027) + science (0.026) + environment (0.023) + additional (0.022) + web (0.022) + computer (0.019) + technical (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic6:\u003c/p\u003e\n \u003cp\u003eEconomic Engineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003eeconomics (0.049) + engineering (0.049) + master (0.041) + additional (0.032) + science (0.030) + administration (0.027) + finance (0.026) + invest (0.026) + professional (0.024) + environmental (0.024) + business (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic7:\u003c/p\u003e\n \u003cp\u003eBusiness and Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ebusiness (0.046) + communication (0.025) + economics (0.025) + financial (0.022) + environment (0.020) + leadership (0.018) + analysis (0.017) + senior (0.017) + external (0.016) + professional (0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic8:\u003c/p\u003e\n \u003cp\u003eRespect for Diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ecommunication (0.035) + diversity (0.034) + commitment (0.028) + environment (0.028) + respect (0.027) + sensitivity (0.024) + willingness (0.022) + training (0.019) + values (0.019) + accountability (0.016) + initiative (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic9:\u003c/p\u003e\n \u003cp\u003eHumanitarian Coordination and Crisis Response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ehumanitarian (0.141) + coordination (0.041) + professional (0.040) + relations (0.037) + fluency (0.032) + communication (0.027) + response (0.026) + language (0.021) + science (0.020) + emergency (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic10:\u003c/p\u003e\n \u003cp\u003eDigital Media Proficiency and Technical Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003emedia (0.070) + communication (0.049) + professional (0.046) + software (0.028) + digital (0.028) + design (0.027) + language (0.021) + relations (0.019) + video (0.017) + journalism (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic11:\u003c/p\u003e\n \u003cp\u003eDada Processing and Statistical Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003edata (0.163) + analysis (0.066) + software (0.036) + collection (0.030) + science (0.027) + statistics (0.022) + communication (0.021) + evaluation (0.018) + computer (0.017) + statistical (0.016) + technical (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic12:\u003c/p\u003e\n \u003cp\u003eBasic Office skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ecommunication (0.051) + environment (0.040) + excel (0.028) + attention (0.024) + discretion (0.022) + oral (0.022) + pressure (0.019) + initiative (0.018) + suite (0.017) + writing (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic13:\u003c/p\u003e\n \u003cp\u003eBusiness Strategy and Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ebusiness (0.097) + administration (0.081) + communication (0.043) + fluency (0.021) + professional (0.020) + language (0.018) + excel (0.017) + administrative (0.016) + environment (0.014) + oral (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic14:\u003c/p\u003e\n \u003cp\u003eTechnology Background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003etechnical (0.078) + evaluation (0.044) + capacity (0.033) + training (0.022) + communication (0.020) + design (0.016) + fluency (0.016) + software (0.015) + master (0.014) + cultural (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic15:\u003c/p\u003e\n \u003cp\u003eEngineering Background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003eprofessional (0.058) + engineering (0.047) + application (0.041) + eligible (0.037) + master (0.034) + design (0.033) + associate (0.032) + software (0.031) + electrical (0.030) + technical (0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic16:\u003c/p\u003e\n \u003cp\u003eLegal Language Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003elanguage (0.182) + professional (0.091) + law (0.068) + support (0.028) + legal (0.026) + environment (0.022) + training (0.016) + programme (0.016) + specific (0.015) + science (0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic17:\u003c/p\u003e\n \u003cp\u003eTechnical Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003elanguage (0.081) + professional (0.027) + completion (0.026) + secondary (0.024) + certificate (0.023) + technical (0.022) + communication (0.022) + fluency (0.022) + oral (0.018) + support (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTopic18:\u003c/p\u003e\n \u003cp\u003eCommunity-Based Technical Governance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 362px;\"\u003e\n \u003cp\u003ecommunity (0.057) + energy (0.048) + nutrition (0.032) + manager (0.023) + technical (0.023) + communication (0.023) + scope (0.023) + department (0.021) + technology (0.019) + leadership (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe Intertopic Distance Map (Fig. 4) illustrates the distribution of all 18 topics extracted from the LDA modeling of STEM posts, confirming that the topics are well separated and conceptually distinct. Fig. 4 highlights the most significant topic as an example, showing its clear spatial distinction while maintaining moderate proximity to related clusters.\u003c/p\u003e\n\u003ch3\u003eLDA Topic Modeling for competencies required by Non-STEM Posts\u003c/h3\u003e\n\u003cp\u003eLDA topic modeling of non-STEM positions identifies eight distinct competency clusters that span functional expertise and cross-cutting skills. These topics highlight the importance of communication, cultural adaptability, ethical conduct, and leadership alongside domain-specific abilities in finance, media, and digital design. These competencies equip professionals to navigate complex organizational environments, foster inclusive collaboration, and contribute effectively to the UN\u0026rsquo;s operational priorities and sustainable development objectives.\u003c/p\u003e\n\u003cp\u003eTopic 1\u0026nbsp;(Cross-Cultural Program Communication), constituting 14.66% of the corpus, emphasizes competencies in cross-cultural communication and regulatory adherence with key terms like \u0026ldquo;program\u0026rdquo;, \u0026ldquo;communication\u0026rdquo;, and \u0026ldquo;language\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eTopic 2\u0026nbsp;(Humanitarianism), constituting 17.18% of the corpus with key terms \u0026ldquo;humanitarian\u0026rdquo; \u0026ldquo;fluency\u0026rdquo;, \u0026ldquo;technical\u0026rdquo;, \u0026ldquo;emergency\u0026rdquo;, and \u0026ldquo;support\u0026rdquo;, setting standards for candidates to understand core humanitarian principles.\u003c/p\u003e\n\u003cp\u003eRepresenting 17.19% of the corpus, Topic 3\u0026nbsp;(Finance and Accounting) focuses on fiscal accuracy and procedural compliance. The keywords \u0026ldquo;financial\u0026rdquo;, \u0026ldquo;communication\u0026rdquo;, \u0026ldquo;accounting\u0026rdquo;, and \u0026ldquo;finance\u0026rdquo; indicate the competency of related financial knowledge.\u003c/p\u003e\n\u003cp\u003eTopic 4(Professional Language Proficiency), constituting 20.61%, the largest proportion, of the corpus, this topic has terms \u0026ldquo;language\u0026rdquo;, \u0026ldquo;business\u0026rdquo;, \u0026ldquo;finance\u0026rdquo;, \u0026ldquo;professional\u0026rdquo;, and \u0026ldquo;administration\u0026rdquo;. Candidates are competent for professional negotiation.\u003c/p\u003e\n\u003cp\u003eAt 5.53% of the corpus, Topic 5\u0026nbsp;(Media and Social Responsibility) has keywords \u0026ldquo;media\u0026rdquo;, \u0026ldquo;energy\u0026rdquo;, \u0026ldquo;environment\u0026rdquo;, \u0026ldquo;exploitation\u0026rdquo;, \u0026ldquo;master\u0026rdquo;, and \u0026ldquo;society\u0026rdquo;. This means employees should be aware of the social impact on media and take related responsibilities.\u003c/p\u003e\n\u003cp\u003eTopic 6\u0026nbsp;(Commitment and Diversity) represents 9.62% of the corpus, with terms \u0026ldquo;commitment\u0026rdquo;, \u0026ldquo;communication\u0026rdquo;, \u0026ldquo;diversity\u0026rdquo;, \u0026ldquo;integrity\u0026rdquo;, \u0026ldquo;values\u0026rdquo;, and \u0026ldquo;cultural\u0026rdquo; in ethical decision-making and cross-cultural communication.\u003c/p\u003e\n\u003cp\u003eAt 10.28% of the corpus, Topic 7\u0026nbsp;(Design Skills and Responsibility) emphasizes digital creativity and devotion to their designing works. Terms like \u0026ldquo;design\u0026rdquo;, \u0026ldquo;online\u0026rdquo;, and \u0026ldquo;media\u0026rdquo; reflect expertise in digital content creation, and \u0026ldquo;security\u0026rdquo; \u0026ldquo;volunteer\u0026rdquo; and \u0026ldquo;responsible\u0026rdquo; link to respect for the position.\u003c/p\u003e\n\u003cp\u003eTopic 8\u0026nbsp;(Leadership and Professionalism). Constituting 8.71% of the corpus, this topic targets higher-level competencies with terms like \u0026ldquo;leadership\u0026rdquo;, \u0026ldquo;senior\u0026rdquo;, \u0026ldquo;society\u0026rdquo;, \u0026ldquo;master\u0026rdquo;, and \u0026ldquo;professional\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eTable 7 summarises the eight topics identified from the LDA analysis of non-STEM job postings, outlining their relative proportions and the principal terms associated with each theme.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 7 Results of LDA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eopic modeling for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003eon-STEM posts\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 354px;\"\u003e\n \u003cp\u003eContent (Weight)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic1:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCross-Cultural Program Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e14.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003eprogramme (0.052) + communication (0.049) + language (0.044) + law (0.031) + professional (0.028) + environment (0.027) + relations (0.023) + training (0.019) + media (0.018) + cultural (0.017) + computer (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic2:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHumanitarianism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e17.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003ehumanitarian (0.113) + fluency (0.034) + technical (0.028) + emergency (0.026) + support (0.024) + leadership (0.023) + communication (0.023) + expertise (0.020) + manager (0.018) + coordination (0.018) + security (0.018) + response (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic3:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFinance and Accounting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e13.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003efinancial (0.077) + communication (0.041) + accounting (0.031) + attention (0.027) + finance (0.026) + excel (0.026) + pressure (0.021) + reporting (0.017) + data (0.017) + professional (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic4:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProfessional Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e20.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003elanguage (0.062) + business (0.051) + finance (0.045) + professional (0.040) + administration (0.035) + financial (0.034) + accounting (0.030) + fluency (0.026) + software (0.022) + master (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic5:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMedia and Information Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003emedia (0.038) + energy (0.032) + environment (0.022) + exploitation (0.019) + master (0.018) + society (0.017) + community (0.017) + capacity (0.017) + humanitarian (0.016) + respect (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic6:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCommitment and Diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e9.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003ecommitment (0.042) + communication (0.034) + diversity (0.027) + integrity (0.027) + values (0.022) + cultural (0.022) + orientation (0.020) + awareness (0.019) + planning (0.019) + accountability (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic7:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDesign Skills and Responsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e10.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003edesign (0.041) + online (0.033) + media (0.030) + security (0.024) + volunteer (0.023) + technical (0.022) + responsible (0.020) + digital (0.019) + support (0.019) + video (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTopic8:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLeadership and Professionalism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003eleadership (0.052) + senior (0.033) + fluency (0.025) + communication (0.023) + master (0.023) + professional (0.023) + society (0.021) + oral (0.019) + climate (0.019) + capacity (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe Intertopic Distance Map (Fig. 5) presents the spatial distribution of all eight topics derived from LDA modeling of non-STEM posts, showing clear separation and minimal overlap, which indicates strong topic coherence. The most prominent topic is shown here as an illustrative example, demonstrating how the model captures distinct thematic clusters while maintaining meaningful relationships among them.\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003e\u003cstrong\u003eSTEM as the operational core of UN sustainable development efforts\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe finding that approximately 72.8% of UN job postings in 2024 are STEM-designated underscores the UN system’s strong reliance on technical and analytical capabilities to achieve sustainable development. This aligns with the view that STEM expertise is foundational to “Industry, Innovation and Infrastructure” (SDG 9) and “Climate Action” (SDG 13) (UN, 2015). The broad distribution of STEM roles—from Geneva and New York headquarters to field operations in Africa and Asia—illustrates a dual operational model that combines global coordination with local implementation, supporting Liu et al.’s (2024) argument that STEM functions form the operational backbone of international institutions. Notably, STEM positions outnumber their non-STEM counterparts by a significant margin, with higher average grades and greater promotion prospects. This finding is consistent with Zhang et al.’s (2024) conclusion that the employment prospects of STEM undergraduates generally exceed those of graduates overall. It reflects that one of the most significant interests of STEM career, the outcome expectations (Jiang et al., 2024), can be fulfilled in an international organizations where non-STEM competencies such as language, communication, and management are valued. Projections from the U.S. Bureau of Labor Statistics (2025) indicate that STEM employment will grow by 10.4% by 2033, which nearly triple the growth rate for non-STEM roles (3.6%), and the present study provides clear evidence that this trend is also visible within the UN system.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMultilingualism as an essential competency\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eEnglish dominates across all postings (55%), but the substantially higher combined language requirement in STEM roles (136%) compared to non-STEM (53%) indicates that multilingualism functions as a core enabler rather than an optional skill. This aligns with the “Collaboration \u0026amp; Influence” cluster in the synthesized competency taxonomy, as well as research highlighting language proficiency as a prerequisite for effective cross-cultural collaboration in international contexts (Quendler \u0026amp; Lamb, 2016). It also supports Lozano et al.’s (2017) view that inclusiveness is a foundational capability within sustainability-oriented careers.\u003c/p\u003e\n\u003cp\u003eIn STEM-focused UN roles, multilingual capability often extends beyond formal communication to encompass technical knowledge transfer, field coordination, and stakeholder negotiation in linguistically diverse environments. This is particularly relevant in field missions in Francophone Africa, the Arab region, and parts of Latin America, where STEM specialists must engage with local authorities, partner agencies, and communities in languages other than English. Candidates from less multilingual regions may therefore face structural disadvantages in competing for STEM positions with significant field engagement. Addressing this gap requires early integration of language training into STEM curricula. This integration should be combined with intercultural communication training, as recommended in Pérez-Foguet et al (2018)to prepare graduates for the complex socio-technical demands of sustainable development projects. Ultimately, multilingualism in STEM roles mean building trust, ensuring accurate technical exchange, and fostering inclusive participation in problem-solving processes central to the UN’s sustainable development mandate.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTechnical depth coupled with delivery \u0026amp; managerial competencies\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe distribution of positions across professional levels reveals a marked structural difference between STEM and non-STEM roles in the UN system (Fig. 2; Table 4). STEM positions are consistently overrepresented at higher professional grades, with 73.6% of mid-level, 77.1% of senior professional, and 70.1% of director and top executive posts falling within STEM domains. By contrast, non-STEM positions dominate internship opportunities (55.3%), suggesting that initial entry points into the UN system are more accessible for candidates from non-technical fields. This pattern indicates that the pathway to senior leadership in the UN increasingly depends on mastery of both advanced technical competencies and higher-order managerial capabilities, a trend also observed in sustainable development.\u003c/p\u003e\n\u003cp\u003eAt the senior levels, STEM roles demand an integrated portfolio of competencies combining Technical \u0026amp; Analytical capabilities (e.g., technological awareness, scientific credibility, advanced data analytics) with Delivery \u0026amp; Results competencies (planning, accountability, resource mobilisation) and Managerial competencies (vision, decision-making, people management) as defined in the UNCF framework. This integration reflects the UN’s operational imperative to address complex, technology-driven mandates in areas such as climate adaptation, digital infrastructure, and global health security—sectors closely linked to SDG 9 (Industry, Innovation and Infrastructure), SDG 13 (Climate Action), and SDG 17 (Partnerships for the Goals). Similar competency configurations have been documented in leadership profiles for sustainability-oriented organisations, where technical domain mastery must be matched with adaptive leadership and systems thinking (Wiek et al., 2011). Compared with findings from a recent study, an importance sequence followed by technical theory, engineering design, leadership, communication, problem solving, professionalism, and lifelong learning (Yu et al., 2022) with its Generic Engineering Competencies Questionnaire (GECQ) method, our study showed higher importance in professionalism and lower in engineering design.\u003c/p\u003e\n\u003cp\u003eLDA topic modelling provides further granularity, showing that STEM leadership roles often span interdisciplinary domains. For example, “Economic Engineering” (Topic 6) merges financial and technical decision-making, while “Cybersecurity and Digital Infrastructure Management” (Topic 5) integrates risk governance with technological resilience. These hybrid competency profiles reflect the evolving nature of UN mandates, where leadership involves orchestrating diverse technical teams under political, cultural, and operational constraints. Non-STEM leadership topics, such as “Cross-Cultural Program Communication” and “Commitment and Diversity”, reveal an enduring emphasis on linguistic agility, cultural intelligence, and ethical governance, which remain indispensable for sustaining multilateral collaboration in complex geopolitical environments (Ang et al., 2020).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eGaps between institutional frameworks and recruitment reality\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIt is proved that gaps still exist between STEM education and the required workplace skills, and further researches are focusing on discovering whether frameworks or educational patterns could cover all competencies required in real STEM positions (Zhan et al., 2023). While the synthesized UNCF-aligned taxonomy includes Creativity/Innovation and Trust-Building, those competencies appear less explicitly in recruitment language. Instead, innovation seems embedded in data and digital skill descriptors, and trust-building in coordination or stakeholder engagement terminology. This echoes Wiek \u0026amp; Redman’s (2022) caution that top-down competency frameworks may not fully reflect how organizations operationalize those competencies. It suggests a need for frameworks like UNCF to articulate innovations and relational leadership more directly in job descriptors.\u003c/p\u003e"},{"header":"Implications","content":"\u003ch2\u003e\u003cstrong\u003ePolicy-making for education for sustainable development\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe UN job posting analysis underscores that Education for Sustainable Development policy must be more closely aligned with real-world workforce needs in international organizations. The high demand for STEM expertise, particularly in environmental sciences, information and communications technology, and infrastructure, paired with transversal competencies such as multilingualism, cross-cultural communication, and ethical governance, suggests that Education for Sustainable Development policy frameworks should move beyond content knowledge to emphasize integrated competency profiles. Policymakers at both national and intergovernmental levels can use these insights to embed UN-relevant competencies into national Education for Sustainable Development strategies, thereby preparing graduates for active participation in global sustainability governance. This requires cross-ministerial coordination between education, foreign affairs, and science \u0026amp; technology departments to ensure that technical training is accompanied by value-based and intercultural competencies.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompetency-based curriculum and training\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe findings reveal a clear need for competency-based curriculum design that prepares graduates for hybrid roles in the UN system. In STEM fields, curricula should combine domain expertise with skills in negotiation, policy interpretation, and sustainable innovation. In non-STEM fields, programs should integrate digital literacy, data-informed decision-making, and a foundational awareness of science–policy interfaces. Training providers—whether universities, vocational institutes, or international training academies—should adopt modular, outcomes-oriented designs linked directly to the UN Competencies for the Future (UNCF) framework. Partnerships between higher education institutions and UN agencies can create practice-oriented learning environments, such as simulation-based policy labs or multilingual field assignments, ensuring that graduates possess both the technical depth and the intercultural agility required for UN service.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTheoretical framework based on bottom-up research methods\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study demonstrates how bottom-up, data-driven approaches such as corpus linguistics and LDA topic modeling can enhance existing theoretical frameworks for competency development in global governance. Rather than relying solely on prescriptive models, the analysis of authentic job postings reveals how competencies evolve in response to operational priorities and SDG implementation contexts. This empirical grounding supports the refinement of competency theories toward integrated, context-sensitive models that account for both visible, trainable skills and less visible, values-based attributes. Embedding bottom-up evidence into theory development can produce more adaptive frameworks that remain responsive to shifts in organizational mandates, technological change, and sustainability challenges, offering a stronger basis for aligning ESD policy, curriculum design, and workforce planning.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study offers a comprehensive corpus-based analysis of STEM-related positions in the UN system with the purpose of exploring the relationship between competencies of STEM talents and the sustainable development. Drawing on the corpus of 33.68 billion words from 23190 recruitment postings in 2024, the study integrates corpus analysis and LDA topic modelling to assess competency demands across various agencies, locations, and position levels.\u003c/p\u003e\n\u003cp\u003eThree major findings emerge. First, STEM positions constitute a significant part in all UN postings, indicating the need of technical expertise in sustainable development. Second, STEM positions are more located in Europe and North America with a demand of multilingual proficiency. Third, STEM positions demonstrate a higher possibility of reaching advanced professional levels than the non-STEM counterparts. Fourth, besides the core competencies of communication, professionalism, and teamwork, STEM positions emphasize data analysis, technological expertise, and systems thinking, which have not yet been included by existing UN competency frameworks.\u003c/p\u003e\n\u003cp\u003eThe findings carry the following implications for STEM education especially for sustainable education. On the one hand, the findings particularly underscore the need to align STEM curricula to real-world competencies in problem solving. On the other hand, a more updated competency framework is needed for STEM education for sustainable development, in which STEM talents are playing important roles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research is not without limits. First, this research only collected data from 2024. This is because few websites are available or suitable for large data analyze, some of which already cleaned up recruited and outdated posts, making it difficult to trace past positions. Thus, only the selected website can be considered as qualified for the research. Secondly, the records on the website varies from year to year, so samples of other years except 2024 are too scarce to be studied, making it impossible to conduct diachronic research. Consequently, we cannot trace the variation of STEM proportion, world distribution, or competency in terms of time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was financially supported by the Philosophy and Social Science Planning Project of Guangdong Province [grant number GD23CWY07], and the Guangdong Provincial Undergraduate Universities Teaching Quality and Teaching Reform Project [grant number C9248420].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAleksić, A, Nestić S, Huber M, \u0026amp; Ljepava N (2022) The Assessment of the Key Competences for Lifelong Learning\u0026mdash;The Fuzzy Model Approach for Sustainable Education. \u003cem\u003eSustainability 14\u003c/em\u003e(5): 2686.https://doi.org/https://doi.org/10.3390/su14052686\u003c/li\u003e\n\u003cli\u003eAng, S, Ng K Y, \u0026amp; Rockstuhl T (2020) Cultural Intelligence. In R. J. Sternberg (Ed), \u003cem\u003eThe Cambridge Handbook of Intelligence\u003c/em\u003e. 2 edn, Cambridge University Press, pp 820-845. https://doi.org/10.1017/9781108770422.035\u003c/li\u003e\n\u003cli\u003eAslam, S, Saleem A, Kennedy T J, Kumar T, Parveen K, Akram H, \u0026amp; Zhang B (2022) Identifying the research and trends in STEM education in Pakistan: A systematic literature review. \u003cem\u003eSage Open 12\u003c/em\u003e(3): 21582440221118545.https://doi.org/10.1177/21582440221118545\u003c/li\u003e\n\u003cli\u003eBartram, D (2005) The Great Eight Competencies: A Criterion-Centric Approach to Validation. \u003cem\u003eJournal of Applied Psychology 90\u003c/em\u003e(6): 1185-1203.https://doi.org/10.1037/0021-9010.90.6.1185\u003c/li\u003e\n\u003cli\u003eBreiner, J, Harkness S, Johnson C, \u0026amp; Koehler C (2012) What is STEM? A discussion about Conceptions of STEM in education and partnerships. \u003cem\u003eSchool science and mathematics 112\u003c/em\u003e.https://doi.org/10.1111/j.1949-8594.2011.00109.x\u003c/li\u003e\n\u003cli\u003eBybee, R W (2010) Advancing STEM education: A 2020 vision. \u003cem\u003eTechnology and engineering teacher 70\u003c/em\u003e(1): 30\u0026nbsp;\u003c/li\u003e\n\u003cli\u003eCai, Z, Zhu J, \u0026amp; Tian S (2023) Research Progress of STEM Education Based on Visual Bibliometric Analysis. \u003cem\u003eSage Open 13\u003c/em\u003e(3): 21582440231200157.https://doi.org/10.1177/21582440231200157\u003c/li\u003e\n\u003cli\u003eEnglish Profile (2023) \u003cem\u003eEnglish Vocabulary Profile Online\u003c/em\u003e.https://www.englishprofile.org/wordlists/evp?start=15680.\u003c/li\u003e\n\u003cli\u003eGao, X, Li P, Shen J, \u0026amp; Sun H (2020) Reviewing assessment of student learning in interdisciplinary STEM education. \u003cem\u003eInternational journal of STEM education 7\u003c/em\u003e(1): 24.https://doi.org/10.1186/s40594-020-00225-4\u003c/li\u003e\n\u003cli\u003eGeagea, A, \u0026amp; MacCallum J (2020) Conversations with young people: Using a creative arts outreach programme to access, mobilise and activate capital to navigate to higher education. \u003cem\u003eThe Australian Educational Researcher 47\u003c/em\u003e(5): 797-814.https://doi.org/10.1007/s13384-019-00364-w\u003c/li\u003e\n\u003cli\u003eHeaslip, G, Vaillancourt A, Tatham P, Kov\u0026aacute;cs G, Blackman D, \u0026amp; Henry M C (2019) Supply chain and logistics competencies in humanitarian aid. \u003cem\u003eDisasters 43\u003c/em\u003e(3): 686-708.https://doi.org/10.1111/disa.12361\u003c/li\u003e\n\u003cli\u003eHomberg, A, Klafke N, Glassen K, Loukanova S, \u0026amp; Mahler C (2020) Role competencies in interprofessional undergraduate education in complementary and integrative medicine: A Delphi study. \u003cem\u003eComplementary Therapies in Medicine 54\u003c/em\u003e: 102542.https://doi.org/10.1016/j.ctim.2020.102542\u003c/li\u003e\n\u003cli\u003eInternational Atomic Energy Agency (2025) \u003cem\u003eWorking at the IAEA\u003c/em\u003e.https://www.iaea.org/about/employment/working-at-the-iaea. Accessed August 28th\u003c/li\u003e\n\u003cli\u003eJiang, H, Zhang L, \u0026amp; Zhang W (2024) Influence of career awareness on STEM career interests: examining the roles of self-efficacy, outcome expectations, and gender. \u003cem\u003eInternational Journal of STEM Education 11\u003c/em\u003e(1): 22.https://doi.org/10.1186/s40594-024-00482-7\u003c/li\u003e\n\u003cli\u003eKart, M, \u0026amp; Şimşek H (2024) Defining competencies in curriculum and instruction and developing a new competency model. \u003cem\u003eHumanities and Social Sciences Communications 11\u003c/em\u003e(1): 1496.https://doi.org/10.1057/s41599-024-03917-2\u003c/li\u003e\n\u003cli\u003eKleinschmit, A J, Rosenwald A, Ryder E F, Donovan S, Murdoch B, Grandgenett N F, Pauley M, Triplett E, Tapprich W, \u0026amp; Morgan W (2023) Accelerating STEM education reform: linked communities of practice promote creation of open educational resources and sustainable professional development. \u003cem\u003eInternational Journal of STEM Education 10\u003c/em\u003e(1): 16.https://doi.org/10.1186/s40594-023-00405-y\u003c/li\u003e\n\u003cli\u003eKurz, R, \u0026amp; Bartram D (2008) Competency and Individual Performance: Modelling the World of Work. In., pp 227-255. https://doi.org/10.1002/9780470696736.ch10\u003c/li\u003e\n\u003cli\u003eLin, K-Y, Yeh Y-F, Hsu Y-S, Wu J-Y, Yang K-L, \u0026amp; Wu H-K (2023) STEM education goals in the twenty-first century: Teachers\u0026rsquo; perceptions and experiences. \u003cem\u003eInternational Journal of Technology and Design Education 33\u003c/em\u003e(2): 479-496.https://doi.org/10.1007/s10798-022-09737-2\u003c/li\u003e\n\u003cli\u003eLiu, Y, Zhao H, \u0026amp; Chen Y (2024) A Data-driven Analysis of STEM-related Positions in UN International Organizations. \u003cem\u003eJournal of Humanities, Arts and Social Science\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n\u003cli\u003eLozano, R, Merrill M Y, Sammalisto K, Ceulemans K, \u0026amp; Lozano F J (2017) Connecting competences and pedagogical approaches for sustainable development in higher education: A literature review and framework proposal. \u003cem\u003eSustainability 9\u003c/em\u003e(10): 1889\u0026nbsp;\u003c/li\u003e\n\u003cli\u003eMacKay, M, Ford C, Grant L E, Papadopoulos A, \u0026amp; McWhirter J E (2024) Developing competencies in public health: a scoping review of the literature on developing competency frameworks and student and workforce development. \u003cem\u003eFRONTIERS IN PUBLIC HEALTH 12\u003c/em\u003e Article 1332412.https://doi.org/10.3389/fpubh.2024.1332412\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Foguet, A, Lazzarini B, Gin\u0026eacute; R, Velo E, Boni A, Sierra M, Zolezzi G, \u0026amp; Trimingham R (2018) Promoting sustainable human development in engineering: Assessment of online courses within continuing professional development strategies. \u003cem\u003eJournal of Cleaner Production 172\u003c/em\u003e: 4286-4302.https://doi.org/10.1016/j.jclepro.2017.06.244\u003c/li\u003e\n\u003cli\u003eQuendler, E, \u0026amp; Lamb M (2016) Learning as a lifelong process - Meeting the challenges of the changing employability landscape: Competences, skills and knowledge for sustainable development. \u003cem\u003eInternational Journal of Continuing Engineering Education and Life-Long Learning 26\u003c/em\u003e: 273.https://doi.org/10.1504/IJCEELL.2016.078447\u003c/li\u003e\n\u003cli\u003eŘehůřek, R, \u0026amp; Sojka P (2010) \u003cem\u003eSoftware Framework for Topic Modelling with Large Corpora\u003c/em\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp; https://doi.org/10.13140/2.1.2393.1847\u003c/li\u003e\n\u003cli\u003eShin, E, Yim S, \u0026amp; Koh A-r (2025) Comparison of consumer perceptions of sustainable and ethical fashions pre- and post-COVID-19 using LDA topic modeling. \u003cem\u003eHumanities and Social Sciences Communications 12\u003c/em\u003e(1): 226.https://doi.org/10.1057/s41599-025-04525-4\u003c/li\u003e\n\u003cli\u003eSpencer, L M, \u0026amp; Spencer P S M (1993) \u003cem\u003eCompetence at Work: Models for Superior Performance\u003c/em\u003e.John Wiley \u0026amp; Sons\u003c/li\u003e\n\u003cli\u003eThe United Nations High Commissioner for Refugees (2024) \u003cem\u003eUNHCR Interview Questions: Core Values and Competencies\u003c/em\u003e.https://undpjobs.net/unhcr-interview-questions-core-values-and-competencies. Accessed Augest 16th\u003c/li\u003e\n\u003cli\u003eTse, L (1996) Language brokering in linguistic minority communities: The case of Chinese-and Vietnamese-American students. \u003cem\u003eBilingual research journal 20\u003c/em\u003e(3-4): 485-498\u0026nbsp;\u003c/li\u003e\n\u003cli\u003eU.S. Bureau of Labor Statistics (2025) \u003cem\u003eEmployment in STEM occupations\u003c/em\u003e.https://www.bls.gov/emp/tables/stem-employment.htm.\u003c/li\u003e\n\u003cli\u003eU.S. Immigration and Customs Enforcement (2024) \u003cem\u003eDHS Announces Updated DHS STEM Designated Degree Program List\u003c/em\u003e.https://www.ice.gov/news/releases/dhs-announces-updated-dhs-stem-designated-degree-program-list.\u003c/li\u003e\n\u003cli\u003eUN Office of Human Resources Management (2009) \u003cem\u003eUnited Nations competencies for the future\u003c/em\u003e.https://digitallibrary.un.org/record/699801?ln=en\u0026amp;v=pdf. Accessed April 26th\u003c/li\u003e\n\u003cli\u003eUn Women (2023) \u003cem\u003eApplication and recruitment process\u003c/em\u003e.https://www.unwomen.org/en/about-us/employment/application-process. Accessed August 16th\u003c/li\u003e\n\u003cli\u003eUNDP (n.d.) \u003cem\u003eUNDP Competency Framework \u003c/em\u003ehttps://www.undp.org/careers/what-we-look-for. Accessed August 17th\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eUNESCO Digital Library(2016)\u003c/em\u003e \u003cem\u003eUNESCO\u003c/em\u003e https://unesdoc.unesco.org/ark:/48223/pf0000245056 August 17th\u003c/li\u003e\n\u003cli\u003eUNICEF (n.d.) \u003cem\u003eGet prepared\u003c/em\u003e.https://www.unicef.org/careers/unicef-core-values. Accessed August 29th\u003c/li\u003e\n\u003cli\u003eUNIDO (2022) \u003cem\u003eUNIDO Competencies\u003c/em\u003e.https://dl.hrmacy.ir/wp-content/uploads/2022/07/29190401/UNIDO-2.pdf. Accessed August 29th\u003c/li\u003e\n\u003cli\u003eUralovich, K, Tulakov U, Kubayevich K, Sapaev I, Saylaubaevna S, Beknazarova Z, \u0026amp; Khurramov A (2023) A primary factor in sustainable development and environmental sustainability is environmental education. \u003cem\u003e\u0026nbsp;11\u003c/em\u003e.https://doi.org/10.22124/CJES.2023.7104\u003c/li\u003e\n\u003cli\u003eWiek, A, Keeler L, \u0026amp; Redman C (2011) Key Competencies in Sustainability: a Reference Framework for Academic Program Development. \u003cem\u003eSustainability science 6\u003c/em\u003e: 203-218.https://doi.org/10.1007/s11625-011-0132-6\u003c/li\u003e\n\u003cli\u003eWiek, A, \u0026amp; Redman A (2022) What Do Key Competencies in Sustainability Offer and How to Use Them. In., pp 27-34. https://doi.org/10.1007/978-3-030-91055-6_4\u003c/li\u003e\n\u003cli\u003eYu, T Z, Shang W W, Liu S X, \u0026amp; Zhu J B (2022) How to Assess Generic Competencies: From Sustainable Development Needs among Engineering Graduates in Industry. \u003cem\u003eSustainability 14\u003c/em\u003e(15) Article 9270.https://doi.org/10.3390/su14159270\u003c/li\u003e\n\u003cli\u003eZhan, Z, Li Y, Mei H, \u0026amp; Lyu S (2023) Key competencies acquired from STEM education: gender-differentiated parental expectations. \u003cem\u003eHumanities and Social Sciences Communications 10\u003c/em\u003e(1): 464.https://doi.org/10.1057/s41599-023-01946-x\u003c/li\u003e\n\u003cli\u003eZhang, X, Liu Y, \u0026amp; Lin J (2024) Dilemmas and Pathways of Education of Humanities and Social Sciences from an International Comparative Perspective. \u003cem\u003eChina Higher Education Research\u003c/em\u003e(11): 77-83.https://doi.org/10.16298/j.cnki.1004-3667.2024.11.11\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7494569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7494569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the global sustainable development, more professionals are in demand in. However, little empirical research has examined the role of Science, Technology, Engineering, and Mathematics (STEM) and the competencies required in the positions of United Nations (UN) system. To address this gap, this study conducts a competency-based corpus analysis of 23,190 UN job postings (33.68 billion words) in the year of 2024, with the purpose of examining how STEM positions in UN recruitments align with sustainable development goals under the competency framework. Specifically, the paper investigates (1) the proportion of STEM-oriented vacancies; (2) differences between STEM and Non-STEM roles in job location, language requirements, and position levels; and (3) the competencies typically emphasized in STEM recruitments. Through corpus analysis and Latent Dirichlet Allocation (LDA) topic modeling, the study finds that (1) approximately 72.8% of UN postings are STEM-related, with strong emphasis on technical execution, data literacy, and innovative problem-solving; (2) STEM positions more frequently appear in Europe and North America with higher positions, where additional French or Spanish proficiency are usually required; (3) while professionalism, communication, and teamwork as universal requirements, quantitative reasoning emerges as particularly salient for STEM posts. The findings offer a data-driven foundation for the alignment between UN competency frameworks and the STEM education for sustainable development.","manuscriptTitle":"STEM in Sustainable Development: A Competency-Based Corpus Analysis of UN Job Postings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 08:10:30","doi":"10.21203/rs.3.rs-7494569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-10T21:34:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-05T07:17:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-21T23:46:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T06:58:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-08-30T10:00:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"44bb8b1c-4a53-4d91-9ca9-8c7a94242e5a","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62859950,"name":"Social science/Education"},{"id":62859951,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-02-16T08:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 08:10:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7494569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7494569","identity":"rs-7494569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}