Metaphors they strategize by: A corpus-assisted critical metaphor analysis of the US and UK cybersecurity strategies

Metaphors they strategize by: A corpus-assisted critical metaphor analysis of the US and UK cybersecurity strategies | 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 Metaphors they strategize by: A corpus-assisted critical metaphor analysis of the US and UK cybersecurity strategies Jiamin Pei, Dandi Li, Peipei Kong, Min Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6632546/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study employs a corpus-assisted critical metaphor analysis to compare the use of three conceptual metaphors in the US and UK national cybersecurity strategies: CYBERSECURITY STRATEGY-MAKING NATIONS ARE PEOPLE, CYBERSECURITY IS COOPERATION, and CYBERSECURITY IS COMPETITION. It explores how these metaphors are used as cognitive and discursive strategies to contribute to conceptualizing cybersecurity and reflecting national identities. The analysis reveals that the UK notably utilizes first-person plural pronouns and COMPETITION metaphors, particularly the SPORT metaphor, to position itself as a consensus-seeker and global leader in cyber technologies. In contrast, the US favors COOPERATION metaphors, portraying itself as a supportive ally collaborating with specific capable countries and assisting those with limited cyber capabilities. Both countries frame cybersecurity as a domain requiring multistakeholder governance; however, the US tends to frame it within a militarized context, while the UK is more inclined to approach it as a competitive technological field, evident in their distinct uses of WAR and SPORT metaphors. By situating the three metaphors in the linguistic and socio-political contexts where they reside, this study demonstrates that governments use metaphors to legitimize their cybersecurity policies, distinguish in-groups from out-groups, and convey their cyber governance philosophies. This study posits that not only which metaphors, but what discourse contexts they situate in and why they are used, matters when understanding cybersecurity issues in strategy documents. Humanities/Language and linguistics Social science/Language and linguistics Introduction National cybersecurity strategies, which detail countries’ strategic thinking about setting a clear vision, scope, objectives, and priorities for cybersecurity (ITU, 2021), play a crucial role in framing how cybersecurity is enacted. As a subset of foreign policy documents, their primary communicative purpose is to convey a country’s vision of cybersecurity objectives and its national security identity/image to audiences (Rieker, 2006 ; Trapara, 2013 ). Therefore, examining these strategies helps enhance our understanding of both the nature of cybersecurity and one country’s cybersecurity image. While research exists on cybersecurity metaphors in policy documents, prior studies predominantly concentrate on generative metaphors that “prescribe certain types of solutions”, such as health, ecosystem, architecture, war, and burglar (e.g., Wolff, 2014 ; Slupska and Taddeo, 2020; Slupska, 2020). However, research on how cybersecurity and national identities are represented via metaphors remains limited. This paper aims to investigate how metaphors contribute to conceptualizing cybersecurity and constructing national cybersecurity images in the US and UK cybersecurity strategy documents. These two countries were chosen because they lead the cybersecurity rankings of the International Telecommunications Union (ITU) and have consistently updated their cybersecurity strategies in recent years (ITU, 2021). Cybersecurity is most often tackled from a technological standpoint but entails numerous socioeconomic and political implications (Cheng et al., 2023). Thus, this paper draws upon Charteris-Black’s ( 2004 ) Critical Metaphor Analysis (CMA), emphasizing the integration of linguistic, semantic, cognitive, and pragmatic dimensions to metaphor analysis. The choice of CMA is primarily informed by its focus on the analysis of metaphors in terms of their “ideological and rhetorical components” (Charteris-Black, 2004 , p. 2). Moreover, CMA provides insights into the hidden and potentially unconscious intentions of language users and uncovers the ideological motivations that underpin their use of metaphors (Charteris-Black, 2004 ). Therefore, this paper employs CMA to unpack the US and UK governments’ communicative intentions behind metaphor use, and to elucidate the ideologies, attitudes, and beliefs that shape their metaphorical uses. To achieve the objectives described above, we review existing literature on metaphors in cybersecurity discourse in Section 2 and introduce data and analytical framework in Section 3. Section 4 elaborates on the role of metaphors in conceptualizing cybersecurity and disclosing national identities. Then, Section 5 provides a discussion of the communicative functions and ideological roles of the metaphors. Finally, Section 6 summarizes the key findings. Metaphor use in cybersecurity discourse The metaphor use in cybersecurity discourse has emerged as a topic of interest due to its capacity to facilitate communication about new complex issues and security concepts like cyberspace and cybersecurity. Generally, prior research in this area has centered on three primary domains: (i) metaphors in information technology, particularly the computer and Internet as central arenas for cybersecurity; (ii) the identification of cybersecurity metaphors and the investigation of their strengths and limitations; and (iii) the rhetorical effects and communicative intentions behind the use of these metaphors. The literature on metaphors in information technology is rich, offering significant insights into studies on metaphors in cybersecurity discourse. Both the computer and Internet have been prominently featured in previous studies (e.g., Jamet and Moulin-Lyon, 2010 ; Bulatović et al., 2022 ). For instance, Bulatović et al. ( 2022 ) argue that the computer can be metaphorically represented as a human being, animal, building or place, and workshop, whereas the Internet can be conceptualized as a highwar, ocean, war, and supermarket. Emerging technologies, such as cloud technologies (Lindh and Nolin, 2017 ), big data (Puschmann and Burgess, 2014 ), and artificial intelligence (Barnden and Lee, 2001 ), have also been the subjects of metaphorical analysis. Additionally, the persuasive potential of metaphors in information technologies has garnered scholarly attention. For instance, metaphors in cloud technologies are extensively used by cloud providers to address concerns about cloud computing and acquire legitimacy for this technology (Lindh and Nolin, 2017 ). Researchers also examine technology metaphors across various genres, such as Internet metaphors in law and commentary (Blavin and Cohen, 2002 ), science and technology metaphors in the press and popular scientific magazines (Christidou et al., 2004 ), and metaphors of computers and the Internet in research papers (Bulatović et al., 2022 ). Studies have identified and classified cybersecurity metaphors, revealing their strengths and limitations. Frincke and Bishop (2004) maintained that the computer or network as a fortress, which is the most commonly used metaphor in the computer security field, is useful but not precise, illustrating the need to understand the differences between this metaphor and the realities of securing systems. Building on this, Karas et al. ( 2008 ) categorized cybersecurity metaphors into two main groups: (i) predominant metaphors, including fortress, criminals, and warfare metaphors, and (ii) newer metaphors, including biological, health, market systems, spatial, physical asset protection, and conflict metaphors. Lapointe ( 2011 ) identified additional metaphors in cybersecurity policies, such as cyber ecosystem, public health, battlefield, cyber commons, and domain metaphors, noting that these metaphors offer valuable insights into cyberspace but cannot address the entirety of cybersecurity challenges. Hilton et al. ( 2022 ) contributed by developing a lightweight algorithm to quantitatively identify metaphors in cybersecurity texts. Moreover, Slupska ( 2021 ) argues that the war metaphor, prevalent in cybersecurity policy discourse, may hinder opportunities for global cooperation, while metaphors related to health, ecosystems, and architecture can foster collaborative policymaking. Cyber-words, such as cyber war, cyber espionage and cyber weapons, also carry metaphorical weight, as they assign new phenomena to existing conceptual categories (Slupska, 2020). For instance, the concept of “cyber war” metaphorically transforms cyber conflict into a type of warfare, despite not fulfilling the necessary conditions of traditional kinetic warfare (Slupska, 2020). Existing literature also reveals that cybersecurity metaphors are strategically employed to evoke some rhetorical effects and convey specific intentions. For instance, Karas et al. ( 2008 ) assert that cybersecurity metaphors have been used to deepen understanding of current cybersecurity strategies or to offer innovative solutions to enhancing cybersecurity. The war metaphor, in particular, has been shown to either juxtapose the diverse range of hostile and malicious activities in cyberspace (Lawson, 2012 ) or to allocate the responsibility of defense to national militaries (Wolff, 2014 ). Alongside the war metaphor, the burglar and health metaphors offer important implications regarding the causes, motivations, and appropriate responses to cyber threats (Wolff, 2014 ). Branch’s ( 2020 ) analysis of US cybersecurity policy documents reveals a tendency for the Department of Defense to employ the fundamental metaphor implied by the cyberspace domain to legitimate the military’s role in cybersecurity and the creation of US Cyber Command. These studies underscore the pragmatic and ideological roles of cybersecurity metaphors, reflecting the intentional choices made by policymakers to achieve specific rhetorical objectives within particular contexts. In sum, the exploration of metaphor use in cybersecurity discourse has yielded fruitful insights. However, previous studies often revolve around individual metaphors, which can constrain people’s perceptions. A single metaphor partially structures one concept since the source domains foreground certain aspects of target domains while downplaying or hiding others (Lakoff and Johnson, 1980 ). For instance, the metaphor CYBERSECURITY IS WAR primarily underscores negative aspects of cybersecurity, obscuring potential avenues for cooperation. In this regard, this paper draws upon Morgan’s concept of metaphor family (MF) to investigate the COOPERATION and COMPETITION MFs in the strategy documents. A MF is understood as an abstract schema that unifies a collection of “individual and radially-grouped metaphors” (Morgan 2008 , p. 484). For example, the COMPETITION MF includes individual metaphors such as WAR, SPORT, RACE, GAME, etc. These abstract schemas encompass COOPERATION and COMPETITION, both crucial for describing a variety of human experiences, including life, business, politics, love, and legal systems (Morgan, 2008 ). Cybersecurity is similarly multidimensional, taken as a strategic competitive area among diverse social actors, including countries, private sectors, and attackers, while simultaneously necessitating interstate, interagency, and multistakeholder cooperation to secure cyberspace. As ITU (2021) notes, cybersecurity is not solely a technical challenge; it involves complex dynamics of cooperation and competition among various stakeholders. Notions of cooperation and competition, which are metaphorical in nature, have been researched in business discourse (Rubin, 2014 ; Sun and Jiang, 2014) and international relations (Marks, 2011 ). Furthermore, this paper examines the PEOPLE metaphor in the US and UK cybersecurity strategy documents since this metaphor—like COOPERATION and COMPETITION—is central to shaping national or institutional identities (Sun and Jiang, 2014; Musolff, 2018). While extensive research has investigated cybersecurity metaphors in policy texts, there remains a scarcity of studies conducting linguistic analyses of these metaphors and exploring their relationship with national identities. Methodologically, most previous studies on metaphors in cybersecurity discourse are undertaken using a qualitative descriptive approach, focusing on illustrative examples of metaphors in policy documents. To fill these gaps, we conduct a corpus-assisted CMA to interrogate three conceptual metaphors: STRATEGY-MAKING NATIONS ARE PEOPLE, CYBERESECURITY IS COOPERATION, and CYBERESECURITY IS COMPETITION. Specifically, this paper aims to address three questions: (i) What are the similarities and differences (if any) in the use of these source domains (PEOPLE, COOPERATION, and COMPETITION) between the US and UK national cybersecurity strategies? (ii) What do these similarities and differences (if any) reveal about cybersecurity and the two countries’ identities? (iii) What are the ideological and rhetorical motivations behind these similarities and differences? Research methodology Research methodology Data. Two specialized corpora were compiled for this study: the US corpus including all US cybersecurity strategies extracted from the presidential White House Archives and the official website of the Department of Defense; and the UK corpus comprising all cybersecurity documents extracted from the UK government’s official websites. To ensure comparability between the two corpora, we focused on the national-level strategy documents published by both governments and their respective defense departments. The data utilized in this study include all US and UK cybersecurity documents covering the period from 2003 until August 2024 when the data collection was finished. The year 2003 was chosen since the first national cybersecurity strategy across the world was published by the US in 2003. Detailed information regarding the two corpora is outlined in Table 1 . The US corpus comprises 76,030 tokens and the UK corpus contains 101,069 tokens, as calculated by WordSmith 8.0. Table 1 The composition of the two corpora. Corpus Cybersecurity strategies US The National Strategy to Secure Cyberspace (2003), The Comprehensive National Cybersecurity Initiative (2008), Department of Defense Strategy for Operating in Cyberspace (2011), The DoD Cyber Strategy (2015), National Cyber Strategy (2018), Department of Defense Cyber Strategy (2018), National Cybersecurity Strategy (2023), Cyber Strategy of Department of Defense (2023) UK Cyber Security Strategy of the United Kingdom (2009), The UK Cyber Security Strategy (2011), National Cyber Security Strategy 2016–2021 (2016), National Cyber Strategy 2022 (2021), Government Cyber Security Strategy 2022–2030 (2022), A Cyber Resilience Strategy for Defence (2022) Analytical framework. Following the framework of CMA, we carried out this study from the following three stages: metaphor identification, metaphor interpretation, and metaphor explanation, which are borrowed from Fairclough’s (1995) three-stage Critical Discourse Analysis involving describing, interpreting and explaining textual, discursive and social practices. In the first stage, we utilized Wmatrix 5.0 (Rayson, 2008 ) which offers a web interface to the UCREL Semantic Annotation System (USAS), a tool enabling automatic semantic analysis of texts by assigning semantic fields and tags to lexical items. We first employed Wmatrix to identify the semantic domains that are semantically relevant or subordinate to the three source domains (PEOPLE, COOPERATION, and COMPETITION) by surveying the USAS semantic tagset, which contains 21 major semantic fields and 232 sub-fields. Then, we uploaded each corpus onto Wmatrix for an automatic semantic annotation and used the broad-sweep function to identify all potential semantic tags assigned to each lexical item. Only lemma associated with the target domain (CYBERSECURITY) with a nominal threshold frequency (> 10) in each source domain were retained for further analysis. In the second stage, we inspected whether the lexical items within the identified semantic domains were used as metaphors by following the Metaphor Identification Procedure (MIP) guidelines (Pragglejaz Group, 2007 ). The metaphorical meaning hinges on a comparison between the contextual meaning of one lexical unit and its basic meaning (Krennmayr 2008 ). We retrieved the basic meaning from the Merriam-Webster Dictionary online (MWD) ( https://www.merriam-webster.com/ ) and the Oxford English Dictionary online (OED) ( http://www.oed.com/ ). Each item’s concordance lines were analyzed manually to guarantee the relevance of the metaphorical usage. For instance, in Example (1), the contextual meaning of “cooperation” refers to the public and private sectors’ action of working together, but its basic meaning according to the WMD is “the actions of someone who is being helpful by doing what is wanted or asked for”. The pronoun “someone” emphasizes that “cooperation” is inherently a human activity, while the public and private sectors, represented by “public-private sector” in Example (1), refer to the two kinds of economic units. Thus, “cooperation” is coded as metaphorical since its basic sense relates to human actions. (1) ... its risks must be mitigated through strategic public-private sector cooperation ... Besides, we read through the contexts surrounding each metaphorical expression to ensure that each expression relates to the target issue “CYBERSECURITY”. We observed that a vast majority of metaphorical expressions in the source domains are related to CYBERSECURITY, either the social actors or their activities in cybersecurity discourse. This is because the national cybersecurity strategy has been widely recognized as a genre that plays a critical role in meaning-making about its core topic “cybersecurity”, and all strategies are directed at the ultimate goal of safeguarding national cyberspace (Shafqat, 2016 ). Following the methodology outlined by Sun and Jiang ( 2013 ), the lemmas retrieved from the source domains (PEOPLE, COOPERATION, and COMPETITION) that pertain to the target domain (CYBERSECURITY) were deemed metaphorical. To detect differences in metaphorical usage between the two corpora, we performed chi-square analyses of the frequencies of metaphorical tokens within each domain, setting a p -value threshold of less than 0.05 to indicate a statistically significant difference. It is worth noting that the annotation of words is not always fully accurate, as Wmatrix occasionally fails to identify the semantic meaning of the words in specific contexts. To remedy this limitation, we meticulously reviewed the concordance lines associated with each word to eliminate any incorrectly assigned words. Regarding the third stage (metaphor explanation), we concentrate on disclosing the ideological stances and power relations behind metaphor choices within socio-political and cultural contexts. Specifically, we seek to explain the two countries’ particular ways of using the source domains of PEOPLE, COOPERATION, and COMPETITION to represent CYBERSECURITY and national identities. To facilitate this analysis, we utilized WordSmith 8.0 to query collocates surrounding the metaphor source concepts, as collocation can illuminate “word-meaning associations and assumptions underpinning usage” (Cotter et al., 2021 , p. 19) that may otherwise go unnoticed. We also conducted concordance analyses of the metaphor source concepts since concordance analysis aids in placing them into a contextual frame that elucidates sociopragmatic features of metaphor use (Wikberg, 2008 ; Jing-Schmidt and Peng, 2017 ). Data. Two specialized corpora were compiled for this study: the US corpus including all US cybersecurity strategies extracted from the presidential White House Archives and the official website of the Department of Defense; and the UK corpus comprising all cybersecurity documents extracted from the UK government’s official websites. To ensure comparability between the two corpora, we focused on the national-level strategy documents published by both governments and their respective defense departments. The data utilized in this study include all US and UK cybersecurity documents covering the period from 2003 until August 2024 when the data collection was finished. The year 2003 was chosen since the first national cybersecurity strategy across the world was published by the US in 2003. Detailed information regarding the two corpora is outlined in Table 1 . The US corpus comprises 76,030 tokens and the UK corpus contains 101,069 tokens, as calculated by WordSmith 8.0. Table 1 The composition of the two corpora. Corpus Cybersecurity strategies US The National Strategy to Secure Cyberspace (2003), The Comprehensive National Cybersecurity Initiative (2008), Department of Defense Strategy for Operating in Cyberspace (2011), The DoD Cyber Strategy (2015), National Cyber Strategy (2018), Department of Defense Cyber Strategy (2018), National Cybersecurity Strategy (2023), Cyber Strategy of Department of Defense (2023) UK Cyber Security Strategy of the United Kingdom (2009), The UK Cyber Security Strategy (2011), National Cyber Security Strategy 2016–2021 (2016), National Cyber Strategy 2022 (2021), Government Cyber Security Strategy 2022–2030 (2022), A Cyber Resilience Strategy for Defence (2022) Analytical framework. Following the framework of CMA, we carried out this study from the following three stages: metaphor identification, metaphor interpretation, and metaphor explanation, which are borrowed from Fairclough’s (1995) three-stage Critical Discourse Analysis involving describing, interpreting and explaining textual, discursive and social practices. In the first stage, we utilized Wmatrix 5.0 (Rayson, 2008 ) which offers a web interface to the UCREL Semantic Annotation System (USAS), a tool enabling automatic semantic analysis of texts by assigning semantic fields and tags to lexical items. We first employed Wmatrix to identify the semantic domains that are semantically relevant or subordinate to the three source domains (PEOPLE, COOPERATION, and COMPETITION) by surveying the USAS semantic tagset, which contains 21 major semantic fields and 232 sub-fields. Then, we uploaded each corpus onto Wmatrix for an automatic semantic annotation and used the broad-sweep function to identify all potential semantic tags assigned to each lexical item. Only lemma associated with the target domain (CYBERSECURITY) with a nominal threshold frequency (> 10) in each source domain were retained for further analysis. In the second stage, we inspected whether the lexical items within the identified semantic domains were used as metaphors by following the Metaphor Identification Procedure (MIP) guidelines (Pragglejaz Group, 2007 ). The metaphorical meaning hinges on a comparison between the contextual meaning of one lexical unit and its basic meaning (Krennmayr 2008 ). We retrieved the basic meaning from the Merriam-Webster Dictionary online (MWD) ( https://www.merriam-webster.com/ ) and the Oxford English Dictionary online (OED) ( http://www.oed.com/ ). Each item’s concordance lines were analyzed manually to guarantee the relevance of the metaphorical usage. For instance, in Example (1), the contextual meaning of “cooperation” refers to the public and private sectors’ action of working together, but its basic meaning according to the WMD is “the actions of someone who is being helpful by doing what is wanted or asked for”. The pronoun “someone” emphasizes that “cooperation” is inherently a human activity, while the public and private sectors, represented by “public-private sector” in Example (1), refer to the two kinds of economic units. Thus, “cooperation” is coded as metaphorical since its basic sense relates to human actions. (1) ... its risks must be mitigated through strategic public-private sector cooperation ... Besides, we read through the contexts surrounding each metaphorical expression to ensure that each expression relates to the target issue “CYBERSECURITY”. We observed that a vast majority of metaphorical expressions in the source domains are related to CYBERSECURITY, either the social actors or their activities in cybersecurity discourse. This is because the national cybersecurity strategy has been widely recognized as a genre that plays a critical role in meaning-making about its core topic “cybersecurity”, and all strategies are directed at the ultimate goal of safeguarding national cyberspace (Shafqat, 2016 ). Following the methodology outlined by Sun and Jiang ( 2013 ), the lemmas retrieved from the source domains (PEOPLE, COOPERATION, and COMPETITION) that pertain to the target domain (CYBERSECURITY) were deemed metaphorical. To detect differences in metaphorical usage between the two corpora, we performed chi-square analyses of the frequencies of metaphorical tokens within each domain, setting a p -value threshold of less than 0.05 to indicate a statistically significant difference. It is worth noting that the annotation of words is not always fully accurate, as Wmatrix occasionally fails to identify the semantic meaning of the words in specific contexts. To remedy this limitation, we meticulously reviewed the concordance lines associated with each word to eliminate any incorrectly assigned words. Regarding the third stage (metaphor explanation), we concentrate on disclosing the ideological stances and power relations behind metaphor choices within socio-political and cultural contexts. Specifically, we seek to explain the two countries’ particular ways of using the source domains of PEOPLE, COOPERATION, and COMPETITION to represent CYBERSECURITY and national identities. To facilitate this analysis, we utilized WordSmith 8.0 to query collocates surrounding the metaphor source concepts, as collocation can illuminate “word-meaning associations and assumptions underpinning usage” (Cotter et al., 2021 , p. 19) that may otherwise go unnoticed. We also conducted concordance analyses of the metaphor source concepts since concordance analysis aids in placing them into a contextual frame that elucidates sociopragmatic features of metaphor use (Wikberg, 2008 ; Jing-Schmidt and Peng, 2017 ). Results and analysis The source domain of PEOPLE. In the two corpora, the conceptual metaphor CYBERSECURITY STRATETY-MAKING NATIONS ARE PEOPLE is linguistically manifested in two ways: (i) strategy-making countries are commonly referred to with first-person plural pronouns (such as our, we, us, and ourselves); and (ii) they are described using expressions that convey personal traits. The following subsection illustrates these two aspects of the domain PEOPLE. Within the USAS semantic tagset, the sub-domain semantically relevant to PEOPLE is Z8 (Pronouns). To determine whether the two corpora exhibit differences in their use of the first-person plural pronouns ( our , we , and us ), we calculated the chi-square statistics (See Table 2 ). The pronoun ourselves was excluded due to its frequency being below 10 in either corpus. Results indicate a significant difference in the use of first-person plural pronouns between the two corpora ( \(\:{x}^{2}\) = 244.141, p < .001), with these pronouns occurring more frequently in the UK corpus compared to the US corpus. Table 2 First-person plural pronouns in the two corpora. Pronoun US (Freq.) UK (Freq.) Chi-square Sig. (p) our 405 883 69.872 0.000 we 262 838 165.032 0.000 us 11 69 27.817 0.000 Total 678 1790 244.141 0.000 In political discourse, the first-person plural pronouns are often used by speakers to legitimate their actions and persuade audiences who share common interests and responsibilities with them (Bastow, 2008 ). Though the speaker of all documents in each corpus is the government, the inclusive pronoun we is frequently used on behalf of the speaker and audiences to foster “affiliation and intimacy” (Sun and Jiang, 2014, p. 10) between them. As illustrated in Example (2), the pronoun we extends beyond “the whole nation” to encompass “all stakeholders including government, private sectors and individual citizens”. In Example (3), the pronoun we refers specifically to the UK government, while the italicized our denotes both the UK and its partners. The constant use of inclusive pronouns we and our can be taken as contributing to the discursive strategy of inclusion and thus manufacturing unity and consensus with stakeholders including the UK citizens, the private sectors, and foreign states. Consequently, significant differences in the use of first-person plural pronouns between the two corpora suggest that the UK prefers to position itself as more of an affiliation-seeker and consensus-builder compared to the US. (2) Neither government nor the private sector nor individual citizens can meet this challenge alone– we will expand the ways we work together. (the US corpus) (3) We will deepen existing links with our closest international partners, recognising that this enhances our collective security. (the UK corpus) Next, we extracted semantic domains associated with positive personal traits by referring to the following data: (i) collocate and concordance data of lexical items representing countries, including country names, items signifying governments and their departments and agencies, and first-person plural pronouns; and (ii) a comprehensive survey of all USAS semantic tagsets and the two corpora. Notably, only lemmas relevant to the target domain CYBERSECURITY, particularly those pertaining to cybersecurity strategy-making countries, were retained for further discussion. Ultimately, three sub-domains were identified (see Table 3 ): S8+ (helping), X9.1+ (able/intelligent), and S6+ (strong obligation or necessity), suggesting that the two corpora tend to employ positive self-representation strategies to position themselves as supportive, capable, and responsible. A significant difference was found in the use of X9.1+ ( \(\:{x}^{2}\) = 9.081, p < .01), indicating that the UK more frequently depicts itself as capable compared to the US. These three sub-domains carry evaluative and persuasive freight, eliciting positive evaluations of the two countries and persuading audiences about their favorable images. This finding is consistent with the expectation that self-identity descriptions are generally positive in discourse (van Dijk, 2008). Table 3 Sub-domains (personal traits) of PEOPLE in the two corpora. Item US: Lemma (Freq.) Total UK: Lemma (Freq.) Total Chi-square Sig. ( p ) S8+ support v. (155) support n. (58) help v. (54) encourage (89) 356 support v. (196) support n. (79) help v. (129) encourage (44) 448 0.599 0.439 X9.1+ ability (45) able (21) capability (276) 342 ability (80) able (78) capability (411) 569 10.857 0.001 S6+ responsible (40) responsibility (44) 88 responsible (76) responsibility (75) 151 3.675 0.056 The source domain of COOPERATION. The core members of the COOPERATION MF include FAMILY, FRIENDS, PARTNERS, WORK CREW, SPORTS TEAM, MILITARY UNIT, A COMMUNITY, and AN ANIMAL GROUP (Morgan, 2008 , p. 500). These members share an underlying frame-schema wherein multiple self-willing participants engage in activities collaboratively to achieve a desired mutual goal. The core members (source domains) are employed to conceptualize the construal member of the family, namely the target domain CYBERSECURITY. Within the USAS semantic tagset, three sub-domains are semantically linked to the core members of the COOPERATION MF: S3.1 (Personal relationship), S5+ (Belonging to a group), and S8+ (Helping). Only lemmas (See Table 4 ) referring to the CYBERSECURITY in each source domain were retained for analysis. According to Morgan ( 2008 ), lemmas such as “partnership(s)”, “collective”, “together”, “joint”, “alliance”, “collaboration”, “cooperation”, “collaborative” and “collaborate”, are considered representative of the source domain language. Additional lemmas such as ‘‘partner(s)’’ and ‘‘allies’’ symbolize core members of the family PARTNERS. These lemmas carry positive connotations and evoke favorable attitudes towards relations with cyber actors. To assess whether the two corpora differ in using these three sub-domains semantically associated with core members of the COOPERATION MF, we applied a chi-square test, with results presented in Table 4 . Table 4 Sub-domains of COOPERATION . Item US: Lemma (Freq.) Total UK: Lemma (Freq.) Total Chi-square Sig. ( p ) S3.1 partner n. (200) partner v. (16) 216 partner n. (122) 122 60.808 0.000 S5+ ally n. (99) allied adj. (13) partnership (103) collective (30) together (23) joint (11) alliance (12) 291 partnership (102) together (64) collective (44) ally n. (40) joint (18) join (15) alliance (13) 296 10.609 0.001 S8+ collaboration (60) cooperation (46) collaborative (26) collaborate (19) 151 collaboration (28) collaborative (19) cooperation (11) collaborate (14) 72 55.971 0.000 Total 658 490 97.610 0.000 Table 4 reveals a greater use of metaphorical expressions relevant to the source domain COOPERATION in the US corpus compared to the UK corpus ( \(\:{x}^{2}\) =97.610, p < .001). Specifically, the two corpora differ significantly in each sub-domain, showing the significant overuse of these lemmas in the US corpus compared to the UK corpus. The metaphorical expressions derived from the three sub-domains indicate that the target domain COOPERATION is conceptualized through the core member PARTNERS. To further detect how the two corpora differ in their representation of PARTNERS, we analyzed the ten strongest collocates of partners in each corpus, using a Left4-Right4 span, a minimum collocate frequency of 5, and an MI score ≥ 3 (See Table 5 ). In this study, functional words in collocation lists were removed to retrieve more meaningful patterns. Table 5 Collocates of partners in the two corpora (ranked by MI). Corpus Collocates US allies, interagency, international, working, capacity, ability, industry, foreign, sector, increase, private, global, work, build, develop, agencies, capabilities, states governmental UK allies, international, industry, collective, academia, working, private, business, work, sector, public, ensure, resilience, government, UK A concordance analysis of partners and its collocates in Table 5 shows that the two corpora share three prevalent metaphors. First is “INTERNATIONAL ALLIES AND PARTNERS IN CYBERSECURITY ARE PARTNERS” (see Example 4), as revealed by international and global . This metaphor pertains to social actors explicitly labeled as “all(ies)” and “partner(s)”. Second is “PUBLIC AND PRIVATE SECTORS IN CYBERSECURITY ARE PARTNERS” (see Example 5), as revealed by industry , private , sector , and public . This metaphor focuses on public and private sectors. Third is “MULTISTAKEHOLDERS IN CYBERSECURITY ARE PARTNERS” (see Example 6), as revealed by industry , academia , private , and government . This metaphor concerns stakeholders from at least three sectors, including governments, private sectors, international organizations, academia, and civil society. Therefore, this analysis demonstrates that both countries highlight the significant role of “international allies and partners”, “public and private sectors” and “multistakeholders” as key components of the core member PARTNERS. The collocates interagency and agencies in the US corpus indicate its preference for the metaphor “AGENCIES IN CYBERSECURITY ARE PARTNERS” (see Example 7). (4) DoD will work with key allies and partners to build partner capacity. (5) The Department also provides public and private sector partners with indications ... (6) We will reinforce our core alliances, whilst working with a wider range of partners , including industry, global technical standards bodies, civil society and academia ... (7) ONCD will work with interagency partners to develop ... The collocate international , with a high MI score in both corpora, demonstrates their greater emphasis on “international allies and partners” that could reveal the personalized descriptions of individual countries for conceptualizing national identity (Musolff, 2021 ). We then examined the concordances of allies and partners to identify the geopolitical expressions involving these two words (see Table 6 ) and to investigate how each corpus ascribes various meanings to these two words, particularly how allies and partners are geopolitically distributed. Table 6 The geopolitical expressions involving allies or partners in both corpora. US corpus UK corpus Expressions Freq. Expressions Freq. international partner(s) 20 international allies 47 international allies (and partners) 9 key allies/partners 4 NATO allies 3 international partners 3 (Northeast) Asian allies 2 traditional allies 1 Middle Eastern allies and partners 2 external partners 1 Five Eyes treaty partners 1 like-minded partners 1 global partners 1 As Table 6 shows, both corpora use general and indeterminate descriptors for allies and partners , such as international in the US corpus and international , key , traditional , external , and global in the UK corpus. In comparison, the US corpus tends to use specific terms (e.g., NATO , Asian , Middle Eastern , and Five Eyes treaty ) to geopolitically specify its allies and partners, as in Examples (8)-(10). Compared to the more general terms, these specific geopolitical labels in the US corpus exacerbate the Us-Them dichotomy, reinforcing the metaphorical evaluation of allies and partners . The word support in Examples (8) and (9), which indicates a positively evaluated action, suggests that the US tends to profile itself as a supportive facilitator of its allies’ cyber capabilities, particularly for the Middle Eastern and Asian allies who seek reassurance and assistance. (8) Support the hardening and resiliency of Northeast Asian allies’ networks and systems. As a part of its broader cyber dialogue with Asian allies , ... (9) Support the hardening and resiliency of Middle Eastern allies ’ and partners’ networks... (10) Work with key NATO allies to mitigate cyber risks to DoD and US national interests. In summary, both governments emphasize the importance of multi-level cooperation in safeguarding cybersecurity. However, the statistical analysis shows that the US national identity is more prominently represented in a more COOPERATION-oriented manner than that of the UK. In both corpora, COOPERATION metaphors serve the function of persuading audiences that governments are proactive contributors to the securitization of cyberspace, and that unified efforts across all levels are essential for shaping cybersecurity policies. Furthermore, a closer look at the collocates of partners reveals that in the US corpus, COOPERATION metaphors also perform evaluative functions, evoking a positive self-representation of the ingroups, such as NATO-affiliated countries. Additionally, these metaphors can be utilized to justify distinctions between social actors, such as those who are in need of assistance versus those viewed as equal partners in cooperation. Metaphors both “constrain and reinforce community membership” (Riley and Howard, 1999 , p. 297). By deploying COOPERATION metaphors and the strategy of Us/Them polarization, the US strengthens ties with NATO allies and like-minded countries while indirectly marginalizing countries deemed at odds with its interests. In this light, COOPERATION metaphors in the context of cybersecurity are subtly intertwined with elements of competition. This Us/Them polarization inherent in COOPERATION metaphors not only fosters a sense of community among allies but also heightens a sense of competition and exclusion towards others. The source domain of COMPETITION. The core members of the COMPETITION MF include WAR, SPORT, RACE, GAME, and PREDATION (Morgan, 2008 , p. 492). These members share an underlying frame-schema wherein one of two entities directly struggles to obtain something that only one entity can have. Cybersecurity can be understood through the lenses of both external and internal COMPETITION. For instance, two countries compete to become a leading cyber power, and two institutions vie for governance in cybersecurity issues. In the USAS semantic tagset, five sub-domains were identified to be semantically associated with the core members of the COMPETITION MF: G3 (warfare, defence, and the army; weapons), S7.3+ (competitive), S7.1+ (in power), K5.1 (sports), K5.2 (games), and X9.2+ (success). To determine whether the two corpora differ in their use of these five sub-domains, a chi-square statistic was performed (See Table 7 ). Of note, we excluded the word “strategy” from this list since it largely denotes the documents themselves in each corpus. Table 7 Sub-domains of COMPETITION . Item US: Lemma (Freq.) Total UK: Lemma (Freq.) Total Chi-square Sig. ( p ) G3 attack n. (246) attack v. (14) strategic (143) defend (131) adversary (81) deter (63) deterrence (28) counter (45) target v. (38) target n. (14) attacker (15) combat (19) defense (135) 972 attack n. (286) attack v. (11) defence (94) strategic (123) target v. (46) target n. (40) adversary (88) defend (65) counter (50) deter (37) deterrence (14) offensive (44) offense (14) offender (12) attacker (27) 951 46.017 0.000 S7.1+ lead v. (50) lead n. (10) leader (17) leadership (16) 93 lead v. (52) lead n. (40) leadership (40) leader (28) leading (37) 197 13.988 0.000 K5.1 goal (69) 69 goal (29) tackle (55) 84 0.294 0.588 S7.3+ / 0 competitive (24) competition (13) compete (15) 52 / / X9.2+ successful (18) failure (17) success (17) succeed (10) 62 success (72) successful (35) fail (10) 117 5.031 0.025 Total 1196 1331 20.239 0.000 Table 7 shows that more metaphorical expressions under the domain COMPETITION are used in the US corpus than in the UK corpus ( \(\:{x}^{2}\) = 20.239, p < .001). Besides, the two corpora differ significantly in the use of two sub-domains: G3 ( \(\:{x}^{2}\) = 46.017, p < .001) and S7.1+ ( \(\:{x}^{2}\) = 13.988, p < .01). Specifically, the sub-domains in Table 7 primarily reflect two core members of the COMPETITION MFs: WAR (G3) and SPORT (S7.1+, K5.1, S7.3+, and X9.2+). Although both belong to the same source domain, WAR and SPORT metaphors highlight different aspects of the target domain (Koller, 2004 ): the former emphasizes aggressive aspects of cybersecurity, such as fighting, conflict and strategy, while the latter stresses non-aggressive competitive elements. Results indicate that the US corpus tends to overuse the WAR metaphor (G3) ( \(\:{x}^{2}\) = 46.017, p < .001) whereas the UK corpus is inclined to overuse the SPORT metaphor ( \(\:{x}^{2}\) = 25.964, p < .001). G3 is the most frequently referenced domain in both corpora, consistent with previous research indicating that war metaphors are predominant in cybersecurity policies (Wolff, 2014 ). In this context, competition relates to external conflicts between governments and malicious cyber actors (e.g., nation states, terrorists, criminals, etc.) striving for superior defensive or offensive cybersecurity capabilities. The US’s overuse of G3 suggests a tendency to expand the military’s role in addressing cybersecurity challenges, framing malicious actors like nation states and terrorists as adversaries, and positioning the US as a capable defender of its own and its allies’ interests in cyberspace. This trend is supported by the use of highly frequent metaphorical expressions such as attack , defend , and adversary in the US corpus, as in Examples (12) and (13). The contrastive use of “allies and partners” and “adversaries” in the US corpus emphasizes the positive-negative divide it creates between the like-minded cooperators and cyber attackers and represents the US as a victim. (12) We face adversaries , including nation states and terrorists, who could launch cyber attacks or seek to exploit our systems (13) ... will advance its close cyberspace cooperation with its allies to defend US and allied interests in cyberspace. In contrast, the UK’s overuse of the domain S7.1 + suggests its intention to establish itself as a leader in cybersecurity. The exclusive use of S7.3 + in the UK corpus indicates that the UK government frequently employs general terms like “competitive”, “competition”, and “compete” to characterize the nature of competitive relationships. We further employed WordSmith 8.0 to extract the strongest collocates of lead* in each corpus, using a four-word span on each side of lead* , a minimum collocate frequency of 5, and an MI score ≥ 3 (See Table 8 ). Table 8 Collocates of lead* in the two corpora (ranked by MI). Corpus Collocates US example, effort, DHS, efforts, agencies, federal, development, government, states, department, united, cybersecurity UK position, senior, vital, departments, pillar, democratic, world, global, example, research, influence, role, actions, technologies, way, provide, take, responsible, development, government’s, response, government, industry, capability, work, defence, national, strategy, cyber As Table 8 shows, both corpora embrace two categories of collocates: (i) those tied to competitive ambitions and efforts, such as “(lead by) example ” and effort(s) in the US corpus, and “(leading) position/role ”, “ take (the) lead”, “ global / world leader”, “(lead by) example ”, and “(leadership and) influence ” in the UK corpus; and (ii) those associated with governments or agencies, such as DHS , agencies , federal , government , states , department , and united in the US corpus, and departments and government(’s) in the UK corpus. This suggests that by labeling themselves as leaders, both countries aim to construct a national identity that emphasizes cybersecurity competition and underscores the government’s leading role in this competitive landscape. In contrast, the UK corpus features a unique category of collocates, namely those relating to technologies and research and development , contributing to formulating one sub-metaphor, namely CYBERSECURITY IS COMPETITION IN CYBER SCIENCE AND TECHNOLOGIES, as in Examples (14) and (15). (14) The UK is universally acknowledged as a global leader in cyber security research and development , ... (15) We must have ... a thriving regional innovation ecosystem that enables us to take the lead in critical technologies , ... Overall, both corpora utilize the conceptual metaphor CYBERSECURITY IS COMPETITION but employ distinct rhetorical strategies to assert dominance in the cybersecurity domain and shape differing national identities. The statistical analysis reveals that the UK corpus is more competition-oriented than the US corpus. The US’s overuse of G3 constructs a national identity narrative portraying the US as a capable defender of its own and its allies’ interests in cyberspace and a potential victim of cyberattacks. Meanwhile, the UK’s overuse of S7.1 + and collocates of “ lead* ” suggest an effort to forge a national identity centered on leadership in research and development and technology. Furthermore, the two corpora’s statistically significant differences in using the WAR and SPORT metaphors indicate that the US has a tendency to frame cybersecurity as a militarized domain, while the UK approaches it as a competitive arena, particularly in technological spheres. Discussion Metaphoricity, though extensively approached from a cognitive perspective, could be non-cognitively driven by ideological, cultural, and socio-political contextual factors (Koller, 2004 ). Prioritizing security cooperation with highly capable allies and partners such as the NATO alliance has long been a vital component of the US security culture (Michel, 2012 ). The US’s hero-centric national identity revealed by the metaphorical patterns involving allies and partners is indicative of its broader political and security culture (Hanska, 2014 ; Köhler, 2019 ). Consequently, the COOPERATION metaphor can be viewed as a socially constructed product, embodying one country’s security culture and nationalist ideology, and can effectively persuade audiences with shared cybersecurity interests to collaborate. Similarly, the COMPETITION metaphor also has persuasive and ideological potential. The US’s overuse of G3, representing the WAR metaphor, serves a dual purpose: it reinforces the perception of the US as both a capable cybersecurity defender and a potential victim of cyberattacks, while also legitimizing an expanded military’s role in addressing cybersecurity challenges. This focus on militarization aligns with the US’s recognition of cyberspace as the fifth domain of military warfare, alongside land, sea, air, and outer space (Sutton, 2013 ). The US’s greater emphasis on the metaphor “AGENCIES IN CYBERSECURITY ARE PARTNERS” could stem from its special governance structures. With a federal system encompassing numerous federal, state, local, and tribal agencies, the need arises for robust intergovernmental and interagency collaboration (Harknett and Stever, 2009) to address the multifaceted nature of cyber threats and to manage the distinct roles each agency plays in cybersecurity. The UK’s overuse of the SPORT metaphor and S7.1 + may relate to its Post-Brexit Grand Strategy “Global Britain”, as articulated in the policy paper “ Global Britain in a Competitive Age, the Integrated Review of Security, Defence, Development and Foreign Policy ”. This policy paper signifies the UK’s strategic shift from Europe to global countries and regions, aiming for greater international influence (May, 2017 ). In the UK corpus texts, notably the National Cyber Strategy 2022 and Government Cyber Security Strategy 2022–2030 , this policy paper is explicitly referenced as the strategic context for drafting these strategies. The consistent reference to this policy paper in the UK cybersecurity strategy documents is indicative of the strategy of intertextuality and reinforces the UK’s vision of communicating its leadership in the cybersecurity field to the global community. Additionally, this policy paper highlights the UK’s ambition to become a science superpower in response to China’s rapid tech development (Peters, 2023 ), which accounts for the UK’s emphasis on competition in cyber science and technologies, as evidenced by the collocates of lead* . Thus, the COMPETITION metaphor in the UK corpus is instantiated in specific socio-political contexts to legitimize the government’s role as an influential, international leader in the cybersecurity field, particularly in science and technology. Meanwhile, both corpora bear several similarities in their metaphorical usage. First, metaphors in Table 5 show that, in addition to governments, key stakeholders—such as private sectors, technical experts, academia, and civil society—play indispensable roles in safeguarding cyberspace. Both governments are firm supporters of public-private partnerships since most of their critical infrastructure is privately owned and operated (Carr, 2016 ). Moreover, they both favor a decentralized, multistakeholder approach to cybersecurity, one in which Internet policy is set collectively by representatives from the technical community, industries, academia, and public sectors. This approach is quite different from the centralized, multilateral approach, which relies more on government rule-making (Stadnik, 2017 ). This indicates that both governments project their values of Internet governance onto the cybersecurity field. Final remarks This paper employs a corpus-assisted approach to critically compare the three source domains (PEOPLE, COOPERATION, and COMPETITION) in the US and UK cybersecurity strategy documents, and examine their role in conceptualizing the target domain cybersecurity and disclosing national identities. The findings indicate that the first-person plural pronouns and the source domain COMPETITION (particularly the SPORT metaphor) are overused in the UK corpus, suggesting that the UK aims to establish itself as a consensus-seeker, a more competition-oriented country, and an international leader in global cyber governance and cyber technologies. In contrast, the US’s preference for the domain COOPERATION and geopolitically specific PARTNERS reveals that the US tends to represent itself as a more cooperation-oriented country, highlighting alliances with highly capable allies and partners and extending support to those with limited cyber capabilities. Both corpora represent cybersecurity as a domain requiring multistakeholder governance, as illustrated by the collocates of partners presented in Table 5 . However, the US tends to frame cybersecurity as a militarized domain, while the UK is more inclined to depict it as a competitive arena in technological contexts, as manifested by the statistical differences in their use of WAR and SPORT metaphors. The CMA approach to these linguistic metaphors demonstrates their persuasive, evaluative, and ideological power. Specifically, they act as ideological tools strategically employed to persuade the public about the governments’ positive images and cybersecurity practices, evoke positive or negative attitudes towards particular cybersecurity issues, and reflect and disseminate values surrounding cybersecurity governance. Our discussion in Section 5 suggests that the inclination to verbalize metaphorical conceptualizations of cybersecurity and national identities is not all-pervasive throughout the whole global community, but is constrained by the linguistic and socio-political contexts in which metaphors operate. This study offers insights into the metaphor use in cybersecurity discourse, the use of metaphors to demystify national identities, and the broader research on metaphorical framing. First, it is the first study to examine CYBERSECURITY metaphors through the three source domains of PEOPLE, COOPERATION, and COMPETITION. Second, the findings demonstrate that a corpus approach helps to extract metaphorical expressions and reveal typical metaphor uses in policy texts. A simple focus on individual metaphorical expressions is not sufficient for a comprehensive understanding of an issue, and collocation and concordance data can help yield more linguistic contextual information of metaphorical language. Third, this study contributes to a CMA perspective on cybersecurity metaphors. CMA is a useful instrument for tapping into the governments’ attitudes and values hidden behind their choice and use of cybersecurity metaphors. Nevertheless, this study has limitations that warrant further research. It is primarily synchronic, suggesting that future investigations could adopt a diachronic perspective to explore the temporal dynamics of these metaphors and their sociopragmatic functions. Additionally, other oft-used metaphors—such as health, ecosystem, and architecture—deserve further exploration beyond those addressed in this study. Declarations Competing interests The authors declare no competing interests. Ethical approval This article does not contain any studies requiring ethical approval. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution JP: conception and design of the work, writing, revising; JP and MW: gathering and analyzing the data. DL and PK prepared all tables. All authors extensively reviewed and edited the the manuscript. Data availability The datasets analyzed during the current study are available from the corresponding author upon reasonable request. References Barnden JA, Lee MG (eds) (2001) Metaphor and artificial intelligence: a special double issue of metaphor and symbol. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6632546","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486769454,"identity":"0e87a0ee-c453-4c8b-b4fe-cf97ed4a240e","order_by":0,"name":"Jiamin Pei","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Jiamin","middleName":"","lastName":"Pei","suffix":""},{"id":486769455,"identity":"858bd4ef-ff0b-405b-9a22-16753b53553f","order_by":1,"name":"Dandi Li","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Dandi","middleName":"","lastName":"Li","suffix":""},{"id":486769462,"identity":"3f3179c7-dbc0-48cf-be37-64f1b0c104c9","order_by":2,"name":"Peipei Kong","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Kong","suffix":""},{"id":486769464,"identity":"f13daf1d-2efd-48d5-80be-ef6a7c832166","order_by":3,"name":"Min Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3QuwrCMBSA4VOE6HC8jCkp9QmEA4KT1GcpDi4dHDs4WIR0cvc1XJxTCroUfAWzOHXo6iAYNxdpRsH8W+B8uQG4XD8YB/AU0AZZN8t0Y0lAQXoOBljuptyeVJ15yFdyhDbEzytSD8mQ+VqaDaJwsm0hAhMq9jJAJmJ5W8NyOlMtJISEVP99iohz4qDiUysZ1lQ8ZcdcrJAcbYjgCZVYGcI9S+If7usySM/IMDafTBZv4dflUde0WYzzi9ZNGoWtBKBHHwv6OvZZ92Y15nK5XH/cC2/6P113MNqQAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-10 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6632546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6632546/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87067062,"identity":"2a6598d6-347f-4e9b-9ad6-630d1446061a","added_by":"auto","created_at":"2025-07-18 18:46:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6632546/v1/abe54fed-1e28-433e-b60a-c46f3d59ab0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metaphors they strategize by: A corpus-assisted critical metaphor analysis of the US and UK cybersecurity strategies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNational cybersecurity strategies, which detail countries\u0026rsquo; strategic thinking about setting a clear vision, scope, objectives, and priorities for cybersecurity (ITU, 2021), play a crucial role in framing how cybersecurity is enacted. As a subset of foreign policy documents, their primary communicative purpose is to convey a country\u0026rsquo;s vision of cybersecurity objectives and its national security identity/image to audiences (Rieker, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Trapara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, examining these strategies helps enhance our understanding of both the nature of cybersecurity and one country\u0026rsquo;s cybersecurity image. While research exists on cybersecurity metaphors in policy documents, prior studies predominantly concentrate on generative metaphors that \u0026ldquo;prescribe certain types of solutions\u0026rdquo;, such as health, ecosystem, architecture, war, and burglar (e.g., Wolff, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Slupska and Taddeo, 2020; Slupska, 2020). However, research on how cybersecurity and national identities are represented via metaphors remains limited. This paper aims to investigate how metaphors contribute to conceptualizing cybersecurity and constructing national cybersecurity images in the US and UK cybersecurity strategy documents. These two countries were chosen because they lead the cybersecurity rankings of the International Telecommunications Union (ITU) and have consistently updated their cybersecurity strategies in recent years (ITU, 2021).\u003c/p\u003e\u003cp\u003eCybersecurity is most often tackled from a technological standpoint but entails numerous socioeconomic and political implications (Cheng et al., 2023). Thus, this paper draws upon Charteris-Black\u0026rsquo;s (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) Critical Metaphor Analysis (CMA), emphasizing the integration of linguistic, semantic, cognitive, and pragmatic dimensions to metaphor analysis. The choice of CMA is primarily informed by its focus on the analysis of metaphors in terms of their \u0026ldquo;ideological and rhetorical components\u0026rdquo; (Charteris-Black, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, p. 2). Moreover, CMA provides insights into the hidden and potentially unconscious intentions of language users and uncovers the ideological motivations that underpin their use of metaphors (Charteris-Black, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Therefore, this paper employs CMA to unpack the US and UK governments\u0026rsquo; communicative intentions behind metaphor use, and to elucidate the ideologies, attitudes, and beliefs that shape their metaphorical uses.\u003c/p\u003e\u003cp\u003eTo achieve the objectives described above, we review existing literature on metaphors in cybersecurity discourse in Section 2 and introduce data and analytical framework in Section 3. Section 4 elaborates on the role of metaphors in conceptualizing cybersecurity and disclosing national identities. Then, Section 5 provides a discussion of the communicative functions and ideological roles of the metaphors. Finally, Section 6 summarizes the key findings.\u003c/p\u003e\n\u003ch3\u003eMetaphor use in cybersecurity discourse\u003c/h3\u003e\n\u003cp\u003eThe metaphor use in cybersecurity discourse has emerged as a topic of interest due to its capacity to facilitate communication about new complex issues and security concepts like cyberspace and cybersecurity. Generally, prior research in this area has centered on three primary domains: (i) metaphors in information technology, particularly the computer and Internet as central arenas for cybersecurity; (ii) the identification of cybersecurity metaphors and the investigation of their strengths and limitations; and (iii) the rhetorical effects and communicative intentions behind the use of these metaphors.\u003c/p\u003e\u003cp\u003eThe literature on metaphors in information technology is rich, offering significant insights into studies on metaphors in cybersecurity discourse. Both the computer and Internet have been prominently featured in previous studies (e.g., Jamet and Moulin-Lyon, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bulatović et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Bulatović et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argue that the computer can be metaphorically represented as a human being, animal, building or place, and workshop, whereas the Internet can be conceptualized as a highwar, ocean, war, and supermarket. Emerging technologies, such as cloud technologies (Lindh and Nolin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), big data (Puschmann and Burgess, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and artificial intelligence (Barnden and Lee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), have also been the subjects of metaphorical analysis. Additionally, the persuasive potential of metaphors in information technologies has garnered scholarly attention. For instance, metaphors in cloud technologies are extensively used by cloud providers to address concerns about cloud computing and acquire legitimacy for this technology (Lindh and Nolin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Researchers also examine technology metaphors across various genres, such as Internet metaphors in law and commentary (Blavin and Cohen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), science and technology metaphors in the press and popular scientific magazines (Christidou et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and metaphors of computers and the Internet in research papers (Bulatović et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies have identified and classified cybersecurity metaphors, revealing their strengths and limitations. Frincke and Bishop (2004) maintained that the computer or network as a fortress, which is the most commonly used metaphor in the computer security field, is useful but not precise, illustrating the need to understand the differences between this metaphor and the realities of securing systems. Building on this, Karas et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) categorized cybersecurity metaphors into two main groups: (i) predominant metaphors, including fortress, criminals, and warfare metaphors, and (ii) newer metaphors, including biological, health, market systems, spatial, physical asset protection, and conflict metaphors. Lapointe (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) identified additional metaphors in cybersecurity policies, such as cyber ecosystem, public health, battlefield, cyber commons, and domain metaphors, noting that these metaphors offer valuable insights into cyberspace but cannot address the entirety of cybersecurity challenges. Hilton et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) contributed by developing a lightweight algorithm to quantitatively identify metaphors in cybersecurity texts. Moreover, Slupska (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argues that the war metaphor, prevalent in cybersecurity policy discourse, may hinder opportunities for global cooperation, while metaphors related to health, ecosystems, and architecture can foster collaborative policymaking. Cyber-words, such as cyber war, cyber espionage and cyber weapons, also carry metaphorical weight, as they assign new phenomena to existing conceptual categories (Slupska, 2020). For instance, the concept of “cyber war” metaphorically transforms cyber conflict into a type of warfare, despite not fulfilling the necessary conditions of traditional kinetic warfare (Slupska, 2020).\u003c/p\u003e\u003cp\u003eExisting literature also reveals that cybersecurity metaphors are strategically employed to evoke some rhetorical effects and convey specific intentions. For instance, Karas et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) assert that cybersecurity metaphors have been used to deepen understanding of current cybersecurity strategies or to offer innovative solutions to enhancing cybersecurity. The war metaphor, in particular, has been shown to either juxtapose the diverse range of hostile and malicious activities in cyberspace (Lawson, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or to allocate the responsibility of defense to national militaries (Wolff, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Alongside the war metaphor, the burglar and health metaphors offer important implications regarding the causes, motivations, and appropriate responses to cyber threats (Wolff, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Branch’s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) analysis of US cybersecurity policy documents reveals a tendency for the Department of Defense to employ the fundamental metaphor implied by the \u003cem\u003ecyberspace domain\u003c/em\u003e to legitimate the military’s role in cybersecurity and the creation of US Cyber Command. These studies underscore the pragmatic and ideological roles of cybersecurity metaphors, reflecting the intentional choices made by policymakers to achieve specific rhetorical objectives within particular contexts.\u003c/p\u003e\u003cp\u003eIn sum, the exploration of metaphor use in cybersecurity discourse has yielded fruitful insights. However, previous studies often revolve around individual metaphors, which can constrain people’s perceptions. A single metaphor partially structures one concept since the source domains foreground certain aspects of target domains while downplaying or hiding others (Lakoff and Johnson, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). For instance, the metaphor CYBERSECURITY IS WAR primarily underscores negative aspects of cybersecurity, obscuring potential avenues for cooperation. In this regard, this paper draws upon Morgan’s concept of metaphor family (MF) to investigate the COOPERATION and COMPETITION MFs in the strategy documents. A MF is understood as an abstract schema that unifies a collection of “individual and radially-grouped metaphors” (Morgan \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, p. 484). For example, the COMPETITION MF includes individual metaphors such as WAR, SPORT, RACE, GAME, etc. These abstract schemas encompass COOPERATION and COMPETITION, both crucial for describing a variety of human experiences, including life, business, politics, love, and legal systems (Morgan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Cybersecurity is similarly multidimensional, taken as a strategic competitive area among diverse social actors, including countries, private sectors, and attackers, while simultaneously necessitating interstate, interagency, and multistakeholder cooperation to secure cyberspace. As ITU (2021) notes, cybersecurity is not solely a technical challenge; it involves complex dynamics of cooperation and competition among various stakeholders. Notions of cooperation and competition, which are metaphorical in nature, have been researched in business discourse (Rubin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sun and Jiang, 2014) and international relations (Marks, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, this paper examines the PEOPLE metaphor in the US and UK cybersecurity strategy documents since this metaphor—like COOPERATION and COMPETITION—is central to shaping national or institutional identities (Sun and Jiang, 2014; Musolff, 2018). While extensive research has investigated cybersecurity metaphors in policy texts, there remains a scarcity of studies conducting linguistic analyses of these metaphors and exploring their relationship with national identities. Methodologically, most previous studies on metaphors in cybersecurity discourse are undertaken using a qualitative descriptive approach, focusing on illustrative examples of metaphors in policy documents.\u003c/p\u003e\u003cp\u003eTo fill these gaps, we conduct a corpus-assisted CMA to interrogate three conceptual metaphors: STRATEGY-MAKING NATIONS ARE PEOPLE, CYBERESECURITY IS COOPERATION, and CYBERESECURITY IS COMPETITION. Specifically, this paper aims to address three questions: (i) What are the similarities and differences (if any) in the use of these source domains (PEOPLE, COOPERATION, and COMPETITION) between the US and UK national cybersecurity strategies? (ii) What do these similarities and differences (if any) reveal about cybersecurity and the two countries’ identities? (iii) What are the ideological and rhetorical motivations behind these similarities and differences?\u003c/p\u003e"},{"header":"Research methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch methodology\u003c/h2\u003e\u003cp\u003e\u003cb\u003eData.\u003c/b\u003e Two specialized corpora were compiled for this study: the US corpus including all US cybersecurity strategies extracted from the presidential White House Archives and the official website of the Department of Defense; and the UK corpus comprising all cybersecurity documents extracted from the UK government’s official websites. To ensure comparability between the two corpora, we focused on the national-level strategy documents published by both governments and their respective defense departments. The data utilized in this study include all US and UK cybersecurity documents covering the period from 2003 until August 2024 when the data collection was finished. The year 2003 was chosen since the first national cybersecurity strategy across the world was published by the US in 2003. Detailed information regarding the two corpora is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The US corpus comprises 76,030 tokens and the UK corpus contains 101,069 tokens, as calculated by WordSmith 8.0.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe composition of the two corpora.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCybersecurity strategies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe National Strategy to Secure Cyberspace (2003), The Comprehensive National Cybersecurity Initiative (2008), Department of Defense Strategy for Operating in Cyberspace (2011), The DoD Cyber Strategy (2015), National Cyber Strategy (2018), Department of Defense Cyber Strategy (2018), National Cybersecurity Strategy (2023), Cyber Strategy of Department of Defense (2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyber Security Strategy of the United Kingdom (2009), The UK Cyber Security Strategy (2011), National Cyber Security Strategy 2016–2021 (2016), National Cyber Strategy 2022 (2021), Government Cyber Security Strategy 2022–2030 (2022), A Cyber Resilience Strategy for Defence (2022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalytical framework.\u003c/b\u003e Following the framework of CMA, we carried out this study from the following three stages: metaphor identification, metaphor interpretation, and metaphor explanation, which are borrowed from Fairclough’s (1995) three-stage Critical Discourse Analysis involving describing, interpreting and explaining textual, discursive and social practices. In the first stage, we utilized Wmatrix 5.0 (Rayson, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) which offers a web interface to the UCREL Semantic Annotation System (USAS), a tool enabling automatic semantic analysis of texts by assigning semantic fields and tags to lexical items. We first employed Wmatrix to identify the semantic domains that are semantically relevant or subordinate to the three source domains (PEOPLE, COOPERATION, and COMPETITION) by surveying the USAS semantic tagset, which contains 21 major semantic fields and 232 sub-fields. Then, we uploaded each corpus onto Wmatrix for an automatic semantic annotation and used the broad-sweep function to identify all potential semantic tags assigned to each lexical item. Only lemma associated with the target domain (CYBERSECURITY) with a nominal threshold frequency (\u0026gt; 10) in each source domain were retained for further analysis.\u003c/p\u003e\u003cp\u003eIn the second stage, we inspected whether the lexical items within the identified semantic domains were used as metaphors by following the Metaphor Identification Procedure (MIP) guidelines (Pragglejaz Group, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The metaphorical meaning hinges on a comparison between the contextual meaning of one lexical unit and its basic meaning (Krennmayr \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We retrieved the basic meaning from the Merriam-Webster Dictionary online (MWD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.merriam-webster.com/\u003c/span\u003e\u003cspan address=\"https://www.merriam-webster.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Oxford English Dictionary online (OED) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.oed.com/\u003c/span\u003e\u003cspan address=\"http://www.oed.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each item’s concordance lines were analyzed manually to guarantee the relevance of the metaphorical usage. For instance, in Example (1), the contextual meaning of “cooperation” refers to the public and private sectors’ action of working together, but its basic meaning according to the WMD is “the actions of someone who is being helpful by doing what is wanted or asked for”. The pronoun “someone” emphasizes that “cooperation” is inherently a human activity, while the public and private sectors, represented by “public-private sector” in Example (1), refer to the two kinds of economic units. Thus, “cooperation” is coded as metaphorical since its basic sense relates to human actions.\u003c/p\u003e\u003cp\u003e(1) ... its risks must be mitigated through strategic public-private sector \u003cb\u003ecooperation\u003c/b\u003e ...\u003c/p\u003e\u003cp\u003eBesides, we read through the contexts surrounding each metaphorical expression to ensure that each expression relates to the target issue “CYBERSECURITY”. We observed that a vast majority of metaphorical expressions in the source domains are related to CYBERSECURITY, either the social actors or their activities in cybersecurity discourse. This is because the national cybersecurity strategy has been widely recognized as a genre that plays a critical role in meaning-making about its core topic “cybersecurity”, and all strategies are directed at the ultimate goal of safeguarding national cyberspace (Shafqat, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Following the methodology outlined by Sun and Jiang (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the lemmas retrieved from the source domains (PEOPLE, COOPERATION, and COMPETITION) that pertain to the target domain (CYBERSECURITY) were deemed metaphorical.\u003c/p\u003e\u003cp\u003eTo detect differences in metaphorical usage between the two corpora, we performed chi-square analyses of the frequencies of metaphorical tokens within each domain, setting a \u003cem\u003ep\u003c/em\u003e-value threshold of less than 0.05 to indicate a statistically significant difference. It is worth noting that the annotation of words is not always fully accurate, as Wmatrix occasionally fails to identify the semantic meaning of the words in specific contexts. To remedy this limitation, we meticulously reviewed the concordance lines associated with each word to eliminate any incorrectly assigned words.\u003c/p\u003e\u003cp\u003eRegarding the third stage (metaphor explanation), we concentrate on disclosing the ideological stances and power relations behind metaphor choices within socio-political and cultural contexts. Specifically, we seek to explain the two countries’ particular ways of using the source domains of PEOPLE, COOPERATION, and COMPETITION to represent CYBERSECURITY and national identities. To facilitate this analysis, we utilized WordSmith 8.0 to query collocates surrounding the metaphor source concepts, as collocation can illuminate “word-meaning associations and assumptions underpinning usage” (Cotter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 19) that may otherwise go unnoticed. We also conducted concordance analyses of the metaphor source concepts since concordance analysis aids in placing them into a contextual frame that elucidates sociopragmatic features of metaphor use (Wikberg, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jing-Schmidt and Peng, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eData.\u003c/b\u003e Two specialized corpora were compiled for this study: the US corpus including all US cybersecurity strategies extracted from the presidential White House Archives and the official website of the Department of Defense; and the UK corpus comprising all cybersecurity documents extracted from the UK government’s official websites. To ensure comparability between the two corpora, we focused on the national-level strategy documents published by both governments and their respective defense departments. The data utilized in this study include all US and UK cybersecurity documents covering the period from 2003 until August 2024 when the data collection was finished. The year 2003 was chosen since the first national cybersecurity strategy across the world was published by the US in 2003. Detailed information regarding the two corpora is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The US corpus comprises 76,030 tokens and the UK corpus contains 101,069 tokens, as calculated by WordSmith 8.0.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe composition of the two corpora.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCybersecurity strategies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe National Strategy to Secure Cyberspace (2003), The Comprehensive National Cybersecurity Initiative (2008), Department of Defense Strategy for Operating in Cyberspace (2011), The DoD Cyber Strategy (2015), National Cyber Strategy (2018), Department of Defense Cyber Strategy (2018), National Cybersecurity Strategy (2023), Cyber Strategy of Department of Defense (2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyber Security Strategy of the United Kingdom (2009), The UK Cyber Security Strategy (2011), National Cyber Security Strategy 2016–2021 (2016), National Cyber Strategy 2022 (2021), Government Cyber Security Strategy 2022–2030 (2022), A Cyber Resilience Strategy for Defence (2022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalytical framework.\u003c/b\u003e Following the framework of CMA, we carried out this study from the following three stages: metaphor identification, metaphor interpretation, and metaphor explanation, which are borrowed from Fairclough’s (1995) three-stage Critical Discourse Analysis involving describing, interpreting and explaining textual, discursive and social practices. In the first stage, we utilized Wmatrix 5.0 (Rayson, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) which offers a web interface to the UCREL Semantic Annotation System (USAS), a tool enabling automatic semantic analysis of texts by assigning semantic fields and tags to lexical items. We first employed Wmatrix to identify the semantic domains that are semantically relevant or subordinate to the three source domains (PEOPLE, COOPERATION, and COMPETITION) by surveying the USAS semantic tagset, which contains 21 major semantic fields and 232 sub-fields. Then, we uploaded each corpus onto Wmatrix for an automatic semantic annotation and used the broad-sweep function to identify all potential semantic tags assigned to each lexical item. Only lemma associated with the target domain (CYBERSECURITY) with a nominal threshold frequency (\u0026gt; 10) in each source domain were retained for further analysis.\u003c/p\u003e\u003cp\u003eIn the second stage, we inspected whether the lexical items within the identified semantic domains were used as metaphors by following the Metaphor Identification Procedure (MIP) guidelines (Pragglejaz Group, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The metaphorical meaning hinges on a comparison between the contextual meaning of one lexical unit and its basic meaning (Krennmayr \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We retrieved the basic meaning from the Merriam-Webster Dictionary online (MWD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.merriam-webster.com/\u003c/span\u003e\u003cspan address=\"https://www.merriam-webster.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Oxford English Dictionary online (OED) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.oed.com/\u003c/span\u003e\u003cspan address=\"http://www.oed.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each item’s concordance lines were analyzed manually to guarantee the relevance of the metaphorical usage. For instance, in Example (1), the contextual meaning of “cooperation” refers to the public and private sectors’ action of working together, but its basic meaning according to the WMD is “the actions of someone who is being helpful by doing what is wanted or asked for”. The pronoun “someone” emphasizes that “cooperation” is inherently a human activity, while the public and private sectors, represented by “public-private sector” in Example (1), refer to the two kinds of economic units. Thus, “cooperation” is coded as metaphorical since its basic sense relates to human actions.\u003c/p\u003e\u003cp\u003e(1) ... its risks must be mitigated through strategic public-private sector \u003cb\u003ecooperation\u003c/b\u003e ...\u003c/p\u003e\u003cp\u003eBesides, we read through the contexts surrounding each metaphorical expression to ensure that each expression relates to the target issue “CYBERSECURITY”. We observed that a vast majority of metaphorical expressions in the source domains are related to CYBERSECURITY, either the social actors or their activities in cybersecurity discourse. This is because the national cybersecurity strategy has been widely recognized as a genre that plays a critical role in meaning-making about its core topic “cybersecurity”, and all strategies are directed at the ultimate goal of safeguarding national cyberspace (Shafqat, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Following the methodology outlined by Sun and Jiang (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the lemmas retrieved from the source domains (PEOPLE, COOPERATION, and COMPETITION) that pertain to the target domain (CYBERSECURITY) were deemed metaphorical.\u003c/p\u003e\u003cp\u003eTo detect differences in metaphorical usage between the two corpora, we performed chi-square analyses of the frequencies of metaphorical tokens within each domain, setting a \u003cem\u003ep\u003c/em\u003e-value threshold of less than 0.05 to indicate a statistically significant difference. It is worth noting that the annotation of words is not always fully accurate, as Wmatrix occasionally fails to identify the semantic meaning of the words in specific contexts. To remedy this limitation, we meticulously reviewed the concordance lines associated with each word to eliminate any incorrectly assigned words.\u003c/p\u003e\u003cp\u003eRegarding the third stage (metaphor explanation), we concentrate on disclosing the ideological stances and power relations behind metaphor choices within socio-political and cultural contexts. Specifically, we seek to explain the two countries’ particular ways of using the source domains of PEOPLE, COOPERATION, and COMPETITION to represent CYBERSECURITY and national identities. To facilitate this analysis, we utilized WordSmith 8.0 to query collocates surrounding the metaphor source concepts, as collocation can illuminate “word-meaning associations and assumptions underpinning usage” (Cotter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 19) that may otherwise go unnoticed. We also conducted concordance analyses of the metaphor source concepts since concordance analysis aids in placing them into a contextual frame that elucidates sociopragmatic features of metaphor use (Wikberg, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jing-Schmidt and Peng, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and analysis","content":"\u003cp\u003e\u003cb\u003eThe source domain of PEOPLE.\u003c/b\u003e In the two corpora, the conceptual metaphor CYBERSECURITY STRATETY-MAKING NATIONS ARE PEOPLE is linguistically manifested in two ways: (i) strategy-making countries are commonly referred to with first-person plural pronouns (such as our, we, us, and ourselves); and (ii) they are described using expressions that convey personal traits. The following subsection illustrates these two aspects of the domain PEOPLE. Within the USAS semantic tagset, the sub-domain semantically relevant to PEOPLE is Z8 (Pronouns). To determine whether the two corpora exhibit differences in their use of the first-person plural pronouns (\u003cem\u003eour\u003c/em\u003e, \u003cem\u003ewe\u003c/em\u003e, and \u003cem\u003eus\u003c/em\u003e), we calculated the chi-square statistics (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The pronoun \u003cem\u003eourselves\u003c/em\u003e was excluded due to its frequency being below 10 in either corpus. Results indicate a significant difference in the use of first-person plural pronouns between the two corpora (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 244.141, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with these pronouns occurring more frequently in the UK corpus compared to the US corpus.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFirst-person plural pronouns in the two corpora.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePronoun\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUK (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig. (p)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e165.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e244.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn political discourse, the first-person plural pronouns are often used by speakers to legitimate their actions and persuade audiences who share common interests and responsibilities with them (Bastow, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Though the speaker of all documents in each corpus is the government, the inclusive pronoun \u003cem\u003ewe\u003c/em\u003e is frequently used on behalf of the speaker and audiences to foster \u0026ldquo;affiliation and intimacy\u0026rdquo; (Sun and Jiang, 2014, p. 10) between them. As illustrated in Example (2), the pronoun \u003cem\u003ewe\u003c/em\u003e extends beyond \u0026ldquo;the whole nation\u0026rdquo; to encompass \u0026ldquo;all stakeholders including government, private sectors and individual citizens\u0026rdquo;. In Example (3), the pronoun \u003cem\u003ewe\u003c/em\u003e refers specifically to the UK government, while the italicized \u003cem\u003eour\u003c/em\u003e denotes both the UK and its partners. The constant use of inclusive pronouns \u003cem\u003ewe\u003c/em\u003e and \u003cem\u003eour\u003c/em\u003e can be taken as contributing to the discursive strategy of inclusion and thus manufacturing unity and consensus with stakeholders including the UK citizens, the private sectors, and foreign states. Consequently, significant differences in the use of first-person plural pronouns between the two corpora suggest that the UK prefers to position itself as more of an affiliation-seeker and consensus-builder compared to the US.\u003c/p\u003e\u003cp\u003e(2) Neither government nor the private sector nor individual citizens can meet this challenge alone\u0026ndash; \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ewe\u003c/span\u003e will expand the ways \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ewe\u003c/span\u003e work together. (the US corpus)\u003c/p\u003e\u003cp\u003e(3) \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eWe\u003c/span\u003e will deepen existing links with our closest international partners, recognising that this enhances \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eour\u003c/span\u003e collective security. (the UK corpus)\u003c/p\u003e\u003cp\u003eNext, we extracted semantic domains associated with positive personal traits by referring to the following data: (i) collocate and concordance data of lexical items representing countries, including country names, items signifying governments and their departments and agencies, and first-person plural pronouns; and (ii) a comprehensive survey of all USAS semantic tagsets and the two corpora. Notably, only lemmas relevant to the target domain CYBERSECURITY, particularly those pertaining to cybersecurity strategy-making countries, were retained for further discussion. Ultimately, three sub-domains were identified (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): S8+ (helping), X9.1+ (able/intelligent), and S6+ (strong obligation or necessity), suggesting that the two corpora tend to employ positive self-representation strategies to position themselves as supportive, capable, and responsible. A significant difference was found in the use of X9.1+ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 9.081, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), indicating that the UK more frequently depicts itself as capable compared to the US. These three sub-domains carry evaluative and persuasive freight, eliciting positive evaluations of the two countries and persuading audiences about their favorable images. This finding is consistent with the expectation that self-identity descriptions are generally positive in discourse (van Dijk, 2008).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSub-domains (personal traits) of \u003cem\u003ePEOPLE\u003c/em\u003e in the two corpora.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUK: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig. (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esupport \u003cem\u003ev.\u003c/em\u003e (155)\u003c/p\u003e\u003cp\u003esupport \u003cem\u003en.\u003c/em\u003e (58)\u003c/p\u003e\u003cp\u003ehelp \u003cem\u003ev.\u003c/em\u003e (54)\u003c/p\u003e\u003cp\u003eencourage (89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003esupport \u003cem\u003ev.\u003c/em\u003e (196)\u003c/p\u003e\u003cp\u003esupport \u003cem\u003en.\u003c/em\u003e (79)\u003c/p\u003e\u003cp\u003ehelp \u003cem\u003ev.\u003c/em\u003e (129)\u003c/p\u003e\u003cp\u003eencourage (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX9.1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eability (45)\u003c/p\u003e\u003cp\u003eable (21)\u003c/p\u003e\u003cp\u003ecapability (276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eability (80)\u003c/p\u003e\u003cp\u003eable (78)\u003c/p\u003e\u003cp\u003ecapability (411)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS6+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresponsible (40)\u003c/p\u003e\u003cp\u003eresponsibility (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eresponsible (76)\u003c/p\u003e\u003cp\u003eresponsibility (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe source domain of COOPERATION.\u003c/b\u003e The core members of the COOPERATION MF include FAMILY, FRIENDS, PARTNERS, WORK CREW, SPORTS TEAM, MILITARY UNIT, A COMMUNITY, and AN ANIMAL GROUP (Morgan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, p. 500). These members share an underlying frame-schema wherein multiple self-willing participants engage in activities collaboratively to achieve a desired mutual goal. The core members (source domains) are employed to conceptualize the construal member of the family, namely the target domain CYBERSECURITY.\u003c/p\u003e\u003cp\u003eWithin the USAS semantic tagset, three sub-domains are semantically linked to the core members of the COOPERATION MF: S3.1 (Personal relationship), S5+ (Belonging to a group), and S8+ (Helping). Only lemmas (See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) referring to the CYBERSECURITY in each source domain were retained for analysis. According to Morgan (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), lemmas such as \u0026ldquo;partnership(s)\u0026rdquo;, \u0026ldquo;collective\u0026rdquo;, \u0026ldquo;together\u0026rdquo;, \u0026ldquo;joint\u0026rdquo;, \u0026ldquo;alliance\u0026rdquo;, \u0026ldquo;collaboration\u0026rdquo;, \u0026ldquo;cooperation\u0026rdquo;, \u0026ldquo;collaborative\u0026rdquo; and \u0026ldquo;collaborate\u0026rdquo;, are considered representative of the source domain language. Additional lemmas such as \u0026lsquo;\u0026lsquo;partner(s)\u0026rsquo;\u0026rsquo; and \u0026lsquo;\u0026lsquo;allies\u0026rsquo;\u0026rsquo; symbolize core members of the family PARTNERS. These lemmas carry positive connotations and evoke favorable attitudes towards relations with cyber actors. To assess whether the two corpora differ in using these three sub-domains semantically associated with core members of the COOPERATION MF, we applied a chi-square test, with results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSub-domains of \u003cem\u003eCOOPERATION\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUK: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig. (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epartner \u003cem\u003en.\u003c/em\u003e (200)\u003c/p\u003e\u003cp\u003epartner \u003cem\u003ev.\u003c/em\u003e (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epartner \u003cem\u003en.\u003c/em\u003e (122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS5+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eally \u003cem\u003en.\u003c/em\u003e (99)\u003c/p\u003e\u003cp\u003eallied \u003cem\u003eadj.\u003c/em\u003e (13)\u003c/p\u003e\u003cp\u003epartnership (103)\u003c/p\u003e\u003cp\u003ecollective (30)\u003c/p\u003e\u003cp\u003etogether (23)\u003c/p\u003e\u003cp\u003ejoint (11)\u003c/p\u003e\u003cp\u003ealliance (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epartnership (102)\u003c/p\u003e\u003cp\u003etogether (64)\u003c/p\u003e\u003cp\u003ecollective (44)\u003c/p\u003e\u003cp\u003eally \u003cem\u003en.\u003c/em\u003e (40)\u003c/p\u003e\u003cp\u003ejoint (18)\u003c/p\u003e\u003cp\u003ejoin (15)\u003c/p\u003e\u003cp\u003ealliance (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecollaboration (60)\u003c/p\u003e\u003cp\u003ecooperation (46)\u003c/p\u003e\u003cp\u003ecollaborative (26)\u003c/p\u003e\u003cp\u003ecollaborate (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecollaboration (28)\u003c/p\u003e\u003cp\u003ecollaborative (19)\u003c/p\u003e\u003cp\u003ecooperation (11)\u003c/p\u003e\u003cp\u003ecollaborate (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals a greater use of metaphorical expressions relevant to the source domain COOPERATION in the US corpus compared to the UK corpus (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e=97.610, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Specifically, the two corpora differ significantly in each sub-domain, showing the significant overuse of these lemmas in the US corpus compared to the UK corpus. The metaphorical expressions derived from the three sub-domains indicate that the target domain COOPERATION is conceptualized through the core member PARTNERS. To further detect how the two corpora differ in their representation of PARTNERS, we analyzed the ten strongest collocates of \u003cem\u003epartners\u003c/em\u003e in each corpus, using a Left4-Right4 span, a minimum collocate frequency of 5, and an MI score\u0026thinsp;\u0026ge;\u0026thinsp;3 (See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In this study, functional words in collocation lists were removed to retrieve more meaningful patterns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCollocates of \u003cem\u003epartners\u003c/em\u003e in the two corpora (ranked by MI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollocates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eallies, interagency, international, working, capacity, ability, industry, foreign, sector, increase, private, global, work, build, develop, agencies, capabilities, states governmental\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eallies, international, industry, collective, academia, working, private, business, work, sector, public, ensure, resilience, government, UK\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA concordance analysis of \u003cem\u003epartners\u003c/em\u003e and its collocates in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the two corpora share three prevalent metaphors. First is \u0026ldquo;INTERNATIONAL ALLIES AND PARTNERS IN CYBERSECURITY ARE PARTNERS\u0026rdquo; (see Example 4), as revealed by \u003cem\u003einternational\u003c/em\u003e and \u003cem\u003eglobal\u003c/em\u003e. This metaphor pertains to social actors explicitly labeled as \u0026ldquo;all(ies)\u0026rdquo; and \u0026ldquo;partner(s)\u0026rdquo;. Second is \u0026ldquo;PUBLIC AND PRIVATE SECTORS IN CYBERSECURITY ARE PARTNERS\u0026rdquo; (see Example 5), as revealed by \u003cem\u003eindustry\u003c/em\u003e, \u003cem\u003eprivate\u003c/em\u003e, \u003cem\u003esector\u003c/em\u003e, and \u003cem\u003epublic\u003c/em\u003e. This metaphor focuses on public and private sectors. Third is \u0026ldquo;MULTISTAKEHOLDERS IN CYBERSECURITY ARE PARTNERS\u0026rdquo; (see Example 6), as revealed by \u003cem\u003eindustry\u003c/em\u003e, \u003cem\u003eacademia\u003c/em\u003e, \u003cem\u003eprivate\u003c/em\u003e, and \u003cem\u003egovernment\u003c/em\u003e. This metaphor concerns stakeholders from at least three sectors, including governments, private sectors, international organizations, academia, and civil society. Therefore, this analysis demonstrates that both countries highlight the significant role of \u0026ldquo;international allies and partners\u0026rdquo;, \u0026ldquo;public and private sectors\u0026rdquo; and \u0026ldquo;multistakeholders\u0026rdquo; as key components of the core member PARTNERS. The collocates \u003cem\u003einteragency\u003c/em\u003e and \u003cem\u003eagencies\u003c/em\u003e in the US corpus indicate its preference for the metaphor \u0026ldquo;AGENCIES IN CYBERSECURITY ARE PARTNERS\u0026rdquo; (see Example 7).\u003c/p\u003e\u003cp\u003e(4) DoD will work with key \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eallies and partners\u003c/span\u003e to build partner capacity.\u003c/p\u003e\u003cp\u003e(5) The Department also provides \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003epublic and private sector partners\u003c/span\u003e with indications ...\u003c/p\u003e\u003cp\u003e(6) We will reinforce our core alliances, whilst working with a wider range of \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003epartners\u003c/span\u003e, including \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eindustry, global technical standards bodies, civil society and academia\u003c/span\u003e ...\u003c/p\u003e\u003cp\u003e(7) ONCD will work with \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003einteragency\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epartners\u003c/span\u003e to develop ...\u003c/p\u003e\u003cp\u003eThe collocate \u003cem\u003einternational\u003c/em\u003e, with a high MI score in both corpora, demonstrates their greater emphasis on \u0026ldquo;international allies and partners\u0026rdquo; that could reveal the personalized descriptions of individual countries for conceptualizing national identity (Musolff, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We then examined the concordances of \u003cem\u003eallies\u003c/em\u003e and \u003cem\u003epartners\u003c/em\u003e to identify the geopolitical expressions involving these two words (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and to investigate how each corpus ascribes various meanings to these two words, particularly how allies and partners are geopolitically distributed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe geopolitical expressions involving \u003cem\u003eallies\u003c/em\u003e or \u003cem\u003epartners\u003c/em\u003e in both corpora.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUS corpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUK corpus\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpressions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFreq.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpressions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFreq.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einternational partner(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003einternational allies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einternational allies (and partners)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ekey allies/partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNATO allies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003einternational partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Northeast) Asian allies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003etraditional allies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle Eastern allies and partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eexternal partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFive Eyes treaty partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elike-minded partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eglobal partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows, both corpora use general and indeterminate descriptors for \u003cem\u003eallies\u003c/em\u003e and \u003cem\u003epartners\u003c/em\u003e, such as \u003cem\u003einternational\u003c/em\u003e in the US corpus and \u003cem\u003einternational\u003c/em\u003e, \u003cem\u003ekey\u003c/em\u003e, \u003cem\u003etraditional\u003c/em\u003e, \u003cem\u003eexternal\u003c/em\u003e, and \u003cem\u003eglobal\u003c/em\u003e in the UK corpus. In comparison, the US corpus tends to use specific terms (e.g., \u003cem\u003eNATO\u003c/em\u003e, \u003cem\u003eAsian\u003c/em\u003e, \u003cem\u003eMiddle Eastern\u003c/em\u003e, and \u003cem\u003eFive Eyes treaty\u003c/em\u003e) to geopolitically specify its allies and partners, as in Examples (8)-(10). Compared to the more general terms, these specific geopolitical labels in the US corpus exacerbate the Us-Them dichotomy, reinforcing the metaphorical evaluation of \u003cem\u003eallies\u003c/em\u003e and \u003cem\u003epartners\u003c/em\u003e. The word \u003cem\u003esupport\u003c/em\u003e in Examples (8) and (9), which indicates a positively evaluated action, suggests that the US tends to profile itself as a supportive facilitator of its allies\u0026rsquo; cyber capabilities, particularly for the Middle Eastern and Asian allies who seek reassurance and assistance.\u003c/p\u003e\u003cp\u003e(8) \u003cb\u003eSupport\u003c/b\u003e the hardening and resiliency of \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNortheast Asian\u003c/span\u003e allies\u0026rsquo; networks and systems. As a part of its broader cyber dialogue with \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAsian allies\u003c/span\u003e, ...\u003c/p\u003e\u003cp\u003e(9) \u003cb\u003eSupport\u003c/b\u003e the hardening and resiliency of \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMiddle Eastern allies\u003c/span\u003e\u0026rsquo; and partners\u0026rsquo; networks...\u003c/p\u003e\u003cp\u003e(10) Work with key \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNATO allies\u003c/span\u003e to mitigate cyber risks to DoD and US national interests.\u003c/p\u003e\u003cp\u003eIn summary, both governments emphasize the importance of multi-level cooperation in safeguarding cybersecurity. However, the statistical analysis shows that the US national identity is more prominently represented in a more COOPERATION-oriented manner than that of the UK. In both corpora, COOPERATION metaphors serve the function of persuading audiences that governments are proactive contributors to the securitization of cyberspace, and that unified efforts across all levels are essential for shaping cybersecurity policies. Furthermore, a closer look at the collocates of \u003cem\u003epartners\u003c/em\u003e reveals that in the US corpus, COOPERATION metaphors also perform evaluative functions, evoking a positive self-representation of the ingroups, such as NATO-affiliated countries. Additionally, these metaphors can be utilized to justify distinctions between social actors, such as those who are in need of assistance versus those viewed as equal partners in cooperation. Metaphors both \u0026ldquo;constrain and reinforce community membership\u0026rdquo; (Riley and Howard, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, p. 297). By deploying COOPERATION metaphors and the strategy of Us/Them polarization, the US strengthens ties with NATO allies and like-minded countries while indirectly marginalizing countries deemed at odds with its interests. In this light, COOPERATION metaphors in the context of cybersecurity are subtly intertwined with elements of competition. This Us/Them polarization inherent in COOPERATION metaphors not only fosters a sense of community among allies but also heightens a sense of competition and exclusion towards others.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe source domain of COMPETITION.\u003c/b\u003e The core members of the COMPETITION MF include WAR, SPORT, RACE, GAME, and PREDATION (Morgan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, p. 492). These members share an underlying frame-schema wherein one of two entities directly struggles to obtain something that only one entity can have. Cybersecurity can be understood through the lenses of both external and internal COMPETITION. For instance, two countries compete to become a leading cyber power, and two institutions vie for governance in cybersecurity issues. In the USAS semantic tagset, five sub-domains were identified to be semantically associated with the core members of the COMPETITION MF: G3 (warfare, defence, and the army; weapons), S7.3+ (competitive), S7.1+ (in power), K5.1 (sports), K5.2 (games), and X9.2+ (success). To determine whether the two corpora differ in their use of these five sub-domains, a chi-square statistic was performed (See Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Of note, we excluded the word \u0026ldquo;strategy\u0026rdquo; from this list since it largely denotes the documents themselves in each corpus.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSub-domains of \u003cem\u003eCOMPETITION\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUS: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUK: Lemma (Freq.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig. (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eattack \u003cem\u003en.\u003c/em\u003e (246)\u003c/p\u003e\u003cp\u003eattack \u003cem\u003ev.\u003c/em\u003e (14)\u003c/p\u003e\u003cp\u003estrategic (143)\u003c/p\u003e\u003cp\u003edefend (131)\u003c/p\u003e\u003cp\u003eadversary (81)\u003c/p\u003e\u003cp\u003edeter (63)\u003c/p\u003e\u003cp\u003edeterrence (28)\u003c/p\u003e\u003cp\u003ecounter (45)\u003c/p\u003e\u003cp\u003etarget \u003cem\u003ev.\u003c/em\u003e (38)\u003c/p\u003e\u003cp\u003etarget \u003cem\u003en.\u003c/em\u003e (14)\u003c/p\u003e\u003cp\u003eattacker (15)\u003c/p\u003e\u003cp\u003ecombat (19)\u003c/p\u003e\u003cp\u003edefense (135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eattack \u003cem\u003en.\u003c/em\u003e (286)\u003c/p\u003e\u003cp\u003eattack \u003cem\u003ev.\u003c/em\u003e (11)\u003c/p\u003e\u003cp\u003edefence (94)\u003c/p\u003e\u003cp\u003estrategic (123)\u003c/p\u003e\u003cp\u003etarget \u003cem\u003ev.\u003c/em\u003e (46)\u003c/p\u003e\u003cp\u003etarget \u003cem\u003en.\u003c/em\u003e (40)\u003c/p\u003e\u003cp\u003eadversary (88)\u003c/p\u003e\u003cp\u003edefend (65)\u003c/p\u003e\u003cp\u003ecounter (50)\u003c/p\u003e\u003cp\u003edeter (37)\u003c/p\u003e\u003cp\u003edeterrence (14)\u003c/p\u003e\u003cp\u003eoffensive (44)\u003c/p\u003e\u003cp\u003eoffense (14)\u003c/p\u003e\u003cp\u003eoffender (12)\u003c/p\u003e\u003cp\u003eattacker (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS7.1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elead v. (50)\u003c/p\u003e\u003cp\u003elead \u003cem\u003en.\u003c/em\u003e (10)\u003c/p\u003e\u003cp\u003eleader (17)\u003c/p\u003e\u003cp\u003eleadership (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elead \u003cem\u003ev.\u003c/em\u003e (52)\u003c/p\u003e\u003cp\u003elead \u003cem\u003en.\u003c/em\u003e (40)\u003c/p\u003e\u003cp\u003eleadership (40)\u003c/p\u003e\u003cp\u003eleader (28)\u003c/p\u003e\u003cp\u003eleading (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egoal (69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egoal (29)\u003c/p\u003e\u003cp\u003etackle (55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS7.3+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecompetitive (24)\u003c/p\u003e\u003cp\u003ecompetition (13)\u003c/p\u003e\u003cp\u003ecompete (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX9.2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esuccessful (18)\u003c/p\u003e\u003cp\u003efailure (17)\u003c/p\u003e\u003cp\u003esuccess (17)\u003c/p\u003e\u003cp\u003esucceed (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003esuccess (72)\u003c/p\u003e\u003cp\u003esuccessful (35)\u003c/p\u003e\u003cp\u003efail (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that more metaphorical expressions under the domain COMPETITION are used in the US corpus than in the UK corpus (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 20.239, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Besides, the two corpora differ significantly in the use of two sub-domains: G3 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 46.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and S7.1+ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 13.988, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Specifically, the sub-domains in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e primarily reflect two core members of the COMPETITION MFs: WAR (G3) and SPORT (S7.1+, K5.1, S7.3+, and X9.2+). Although both belong to the same source domain, WAR and SPORT metaphors highlight different aspects of the target domain (Koller, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e): the former emphasizes aggressive aspects of cybersecurity, such as fighting, conflict and strategy, while the latter stresses non-aggressive competitive elements. Results indicate that the US corpus tends to overuse the WAR metaphor (G3) (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 46.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) whereas the UK corpus is inclined to overuse the SPORT metaphor (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 25.964, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eG3 is the most frequently referenced domain in both corpora, consistent with previous research indicating that war metaphors are predominant in cybersecurity policies (Wolff, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this context, competition relates to external conflicts between governments and malicious cyber actors (e.g., nation states, terrorists, criminals, etc.) striving for superior defensive or offensive cybersecurity capabilities. The US\u0026rsquo;s overuse of G3 suggests a tendency to expand the military\u0026rsquo;s role in addressing cybersecurity challenges, framing malicious actors like nation states and terrorists as adversaries, and positioning the US as a capable defender of its own and its allies\u0026rsquo; interests in cyberspace. This trend is supported by the use of highly frequent metaphorical expressions such as \u003cem\u003eattack\u003c/em\u003e, \u003cem\u003edefend\u003c/em\u003e, and \u003cem\u003eadversary\u003c/em\u003e in the US corpus, as in Examples (12) and (13). The contrastive use of \u0026ldquo;allies and partners\u0026rdquo; and \u0026ldquo;adversaries\u0026rdquo; in the US corpus emphasizes the positive-negative divide it creates between the like-minded cooperators and cyber attackers and represents the US as a victim.\u003c/p\u003e\u003cp\u003e(12) We face \u003cb\u003eadversaries\u003c/b\u003e, including nation states and terrorists, who could launch \u003cb\u003ecyber attacks\u003c/b\u003e or seek to exploit our systems\u003c/p\u003e\u003cp\u003e(13) ... will advance its close cyberspace cooperation with its allies to \u003cb\u003edefend\u003c/b\u003e US and allied interests in cyberspace.\u003c/p\u003e\u003cp\u003eIn contrast, the UK\u0026rsquo;s overuse of the domain S7.1\u0026thinsp;+\u0026thinsp;suggests its intention to establish itself as a leader in cybersecurity. The exclusive use of S7.3\u0026thinsp;+\u0026thinsp;in the UK corpus indicates that the UK government frequently employs general terms like \u0026ldquo;competitive\u0026rdquo;, \u0026ldquo;competition\u0026rdquo;, and \u0026ldquo;compete\u0026rdquo; to characterize the nature of competitive relationships. We further employed WordSmith 8.0 to extract the strongest collocates of \u003cem\u003elead*\u003c/em\u003e in each corpus, using a four-word span on each side of \u003cem\u003elead*\u003c/em\u003e, a minimum collocate frequency of 5, and an MI score\u0026thinsp;\u0026ge;\u0026thinsp;3 (See Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCollocates of \u003cem\u003elead*\u003c/em\u003e in the two corpora (ranked by MI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorpus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollocates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexample, effort, DHS, efforts, agencies, federal, development, government, states, department, united, cybersecurity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eposition, senior, vital, departments, pillar, democratic, world, global, example, research, influence, role, actions, technologies, way, provide, take, responsible, development, government\u0026rsquo;s, response, government, industry, capability, work, defence, national, strategy, cyber\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows, both corpora embrace two categories of collocates: (i) those tied to competitive ambitions and efforts, such as \u0026ldquo;(lead by) \u003cem\u003eexample\u003c/em\u003e\u0026rdquo; and \u003cem\u003eeffort(s)\u003c/em\u003e in the US corpus, and \u0026ldquo;(leading) \u003cem\u003eposition/role\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003etake\u003c/em\u003e (the) lead\u0026rdquo;, \u0026ldquo;\u003cem\u003eglobal\u003c/em\u003e/\u003cem\u003eworld\u003c/em\u003e leader\u0026rdquo;, \u0026ldquo;(lead by) \u003cem\u003eexample\u003c/em\u003e\u0026rdquo;, and \u0026ldquo;(leadership and) \u003cem\u003einfluence\u003c/em\u003e\u0026rdquo; in the UK corpus; and (ii) those associated with governments or agencies, such as \u003cem\u003eDHS\u003c/em\u003e, \u003cem\u003eagencies\u003c/em\u003e, \u003cem\u003efederal\u003c/em\u003e, \u003cem\u003egovernment\u003c/em\u003e, \u003cem\u003estates\u003c/em\u003e, \u003cem\u003edepartment\u003c/em\u003e, and \u003cem\u003eunited\u003c/em\u003e in the US corpus, and \u003cem\u003edepartments\u003c/em\u003e and \u003cem\u003egovernment(\u0026rsquo;s)\u003c/em\u003e in the UK corpus. This suggests that by labeling themselves as leaders, both countries aim to construct a national identity that emphasizes cybersecurity competition and underscores the government\u0026rsquo;s leading role in this competitive landscape. In contrast, the UK corpus features a unique category of collocates, namely those relating to \u003cem\u003etechnologies\u003c/em\u003e and \u003cem\u003eresearch\u003c/em\u003e and \u003cem\u003edevelopment\u003c/em\u003e, contributing to formulating one sub-metaphor, namely CYBERSECURITY IS COMPETITION IN CYBER SCIENCE AND TECHNOLOGIES, as in Examples (14) and (15).\u003c/p\u003e\u003cp\u003e(14) The UK is universally acknowledged as a global \u003cb\u003eleader\u003c/b\u003e in cyber security \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eresearch and development\u003c/span\u003e, ...\u003c/p\u003e\u003cp\u003e(15) We must have ... a thriving regional innovation ecosystem that enables us to take the \u003cb\u003elead\u003c/b\u003e in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ecritical technologies\u003c/span\u003e, ...\u003c/p\u003e\u003cp\u003eOverall, both corpora utilize the conceptual metaphor CYBERSECURITY IS COMPETITION but employ distinct rhetorical strategies to assert dominance in the cybersecurity domain and shape differing national identities. The statistical analysis reveals that the UK corpus is more competition-oriented than the US corpus. The US\u0026rsquo;s overuse of G3 constructs a national identity narrative portraying the US as a capable defender of its own and its allies\u0026rsquo; interests in cyberspace and a potential victim of cyberattacks. Meanwhile, the UK\u0026rsquo;s overuse of S7.1\u0026thinsp;+\u0026thinsp;and collocates of \u0026ldquo;\u003cem\u003elead*\u003c/em\u003e\u0026rdquo; suggest an effort to forge a national identity centered on leadership in research and development and technology. Furthermore, the two corpora\u0026rsquo;s statistically significant differences in using the WAR and SPORT metaphors indicate that the US has a tendency to frame cybersecurity as a militarized domain, while the UK approaches it as a competitive arena, particularly in technological spheres.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetaphoricity, though extensively approached from a cognitive perspective, could be non-cognitively driven by ideological, cultural, and socio-political contextual factors (Koller, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Prioritizing security cooperation with highly capable allies and partners such as the NATO alliance has long been a vital component of the US security culture (Michel, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The US\u0026rsquo;s hero-centric national identity revealed by the metaphorical patterns involving \u003cem\u003eallies\u003c/em\u003e and \u003cem\u003epartners\u003c/em\u003e is indicative of its broader political and security culture (Hanska, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; K\u0026ouml;hler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the COOPERATION metaphor can be viewed as a socially constructed product, embodying one country\u0026rsquo;s security culture and nationalist ideology, and can effectively persuade audiences with shared cybersecurity interests to collaborate. Similarly, the COMPETITION metaphor also has persuasive and ideological potential. The US\u0026rsquo;s overuse of G3, representing the WAR metaphor, serves a dual purpose: it reinforces the perception of the US as both a capable cybersecurity defender and a potential victim of cyberattacks, while also legitimizing an expanded military\u0026rsquo;s role in addressing cybersecurity challenges. This focus on militarization aligns with the US\u0026rsquo;s recognition of cyberspace as the fifth domain of military warfare, alongside land, sea, air, and outer space (Sutton, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The US\u0026rsquo;s greater emphasis on the metaphor \u0026ldquo;AGENCIES IN CYBERSECURITY ARE PARTNERS\u0026rdquo; could stem from its special governance structures. With a federal system encompassing numerous federal, state, local, and tribal agencies, the need arises for robust intergovernmental and interagency collaboration (Harknett and Stever, 2009) to address the multifaceted nature of cyber threats and to manage the distinct roles each agency plays in cybersecurity.\u003c/p\u003e\u003cp\u003eThe UK\u0026rsquo;s overuse of the SPORT metaphor and S7.1\u0026thinsp;+\u0026thinsp;may relate to its Post-Brexit Grand Strategy \u0026ldquo;Global Britain\u0026rdquo;, as articulated in the policy paper \u0026ldquo;\u003cem\u003eGlobal Britain in a Competitive Age, the Integrated Review of Security, Defence, Development and Foreign Policy\u003c/em\u003e\u0026rdquo;. This policy paper signifies the UK\u0026rsquo;s strategic shift from Europe to global countries and regions, aiming for greater international influence (May, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the UK corpus texts, notably the \u003cem\u003eNational Cyber Strategy 2022\u003c/em\u003e and \u003cem\u003eGovernment Cyber Security Strategy 2022\u0026ndash;2030\u003c/em\u003e, this policy paper is explicitly referenced as the strategic context for drafting these strategies. The consistent reference to this policy paper in the UK cybersecurity strategy documents is indicative of the strategy of intertextuality and reinforces the UK\u0026rsquo;s vision of communicating its leadership in the cybersecurity field to the global community. Additionally, this policy paper highlights the UK\u0026rsquo;s ambition to become a science superpower in response to China\u0026rsquo;s rapid tech development (Peters, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which accounts for the UK\u0026rsquo;s emphasis on competition in cyber science and technologies, as evidenced by the collocates of \u003cem\u003elead*\u003c/em\u003e. Thus, the COMPETITION metaphor in the UK corpus is instantiated in specific socio-political contexts to legitimize the government\u0026rsquo;s role as an influential, international leader in the cybersecurity field, particularly in science and technology.\u003c/p\u003e\u003cp\u003eMeanwhile, both corpora bear several similarities in their metaphorical usage. First, metaphors in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that, in addition to governments, key stakeholders\u0026mdash;such as private sectors, technical experts, academia, and civil society\u0026mdash;play indispensable roles in safeguarding cyberspace. Both governments are firm supporters of public-private partnerships since most of their critical infrastructure is privately owned and operated (Carr, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, they both favor a decentralized, multistakeholder approach to cybersecurity, one in which Internet policy is set collectively by representatives from the technical community, industries, academia, and public sectors. This approach is quite different from the centralized, multilateral approach, which relies more on government rule-making (Stadnik, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indicates that both governments project their values of Internet governance onto the cybersecurity field.\u003c/p\u003e"},{"header":"Final remarks","content":"\u003cp\u003eThis paper employs a corpus-assisted approach to critically compare the three source domains (PEOPLE, COOPERATION, and COMPETITION) in the US and UK cybersecurity strategy documents, and examine their role in conceptualizing the target domain cybersecurity and disclosing national identities. The findings indicate that the first-person plural pronouns and the source domain COMPETITION (particularly the SPORT metaphor) are overused in the UK corpus, suggesting that the UK aims to establish itself as a consensus-seeker, a more competition-oriented country, and an international leader in global cyber governance and cyber technologies. In contrast, the US\u0026rsquo;s preference for the domain COOPERATION and geopolitically specific PARTNERS reveals that the US tends to represent itself as a more cooperation-oriented country, highlighting alliances with highly capable allies and partners and extending support to those with limited cyber capabilities. Both corpora represent cybersecurity as a domain requiring multistakeholder governance, as illustrated by the collocates of \u003cem\u003epartners\u003c/em\u003e presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. However, the US tends to frame cybersecurity as a militarized domain, while the UK is more inclined to depict it as a competitive arena in technological contexts, as manifested by the statistical differences in their use of WAR and SPORT metaphors.\u003c/p\u003e\u003cp\u003eThe CMA approach to these linguistic metaphors demonstrates their persuasive, evaluative, and ideological power. Specifically, they act as ideological tools strategically employed to persuade the public about the governments\u0026rsquo; positive images and cybersecurity practices, evoke positive or negative attitudes towards particular cybersecurity issues, and reflect and disseminate values surrounding cybersecurity governance. Our discussion in Section 5 suggests that the inclination to verbalize metaphorical conceptualizations of cybersecurity and national identities is not all-pervasive throughout the whole global community, but is constrained by the linguistic and socio-political contexts in which metaphors operate.\u003c/p\u003e\u003cp\u003eThis study offers insights into the metaphor use in cybersecurity discourse, the use of metaphors to demystify national identities, and the broader research on metaphorical framing. First, it is the first study to examine CYBERSECURITY metaphors through the three source domains of PEOPLE, COOPERATION, and COMPETITION. Second, the findings demonstrate that a corpus approach helps to extract metaphorical expressions and reveal typical metaphor uses in policy texts. A simple focus on individual metaphorical expressions is not sufficient for a comprehensive understanding of an issue, and collocation and concordance data can help yield more linguistic contextual information of metaphorical language. Third, this study contributes to a CMA perspective on cybersecurity metaphors. CMA is a useful instrument for tapping into the governments\u0026rsquo; attitudes and values hidden behind their choice and use of cybersecurity metaphors. Nevertheless, this study has limitations that warrant further research. It is primarily synchronic, suggesting that future investigations could adopt a diachronic perspective to explore the temporal dynamics of these metaphors and their sociopragmatic functions. Additionally, other oft-used metaphors\u0026mdash;such as health, ecosystem, and architecture\u0026mdash;deserve further exploration beyond those addressed in this study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies requiring ethical approval.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJP: conception and design of the work, writing, revising; JP and MW: gathering and analyzing the data. DL and PK prepared all tables. All authors extensively reviewed and edited the the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarnden JA, Lee MG (eds) (2001) Metaphor and artificial intelligence: a special double issue of metaphor and symbol. Psychology Press, New York\u003c/li\u003e\n\u003cli\u003eBastow T (2008) Defence discourse II: a corpus perspective on routine and rhetoric in defence discourse. In: Mayr A (ed) Language and power: an introduction to institutional discourse. Continuum, London, pp. 138-162\u003c/li\u003e\n\u003cli\u003eBiden JRJ (2020) Why America must lead again. Foreign Aff. 99(2): 64-76\u003c/li\u003e\n\u003cli\u003eBlavin JH, Cohen IG (2002) Gore, Gibson, and Goldsmith: the evolution of Internet metaphors in law and commentary. Harv. J. 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LXIV(1149): 5-34\u003c/li\u003e\n\u003cli\u003eWikberg K (2008) The role of corpus studies in metaphor research. In: Johannesson N, Minugh DC (eds), Selected papers from the 2006 and 2007 Stockholm Metaphor Festivals. Department of English, Stockholm University, Stockholm, pp. 33-48\u003c/li\u003e\n\u003cli\u003eWolff J (2014) Cybersecurity as metaphor: policy and defense implications of computer security metaphors. TPRC Conference Paper. https://papers.ssrn.com/sol3/Deliver\u003c/li\u003e\n\u003cli\u003ey.cfm/SSRN_ID2481133_code1832498.pdf?abstracti=2418638\u0026amp;mirid=1. Accessed 30 December 2024\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-6632546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6632546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study employs a corpus-assisted critical metaphor analysis to compare the use of three conceptual metaphors in the US and UK national cybersecurity strategies: CYBERSECURITY STRATEGY-MAKING NATIONS ARE PEOPLE, CYBERSECURITY IS COOPERATION, and CYBERSECURITY IS COMPETITION. It explores how these metaphors are used as cognitive and discursive strategies to contribute to conceptualizing cybersecurity and reflecting national identities. The analysis reveals that the UK notably utilizes first-person plural pronouns and COMPETITION metaphors, particularly the SPORT metaphor, to position itself as a consensus-seeker and global leader in cyber technologies. In contrast, the US favors COOPERATION metaphors, portraying itself as a supportive ally collaborating with specific capable countries and assisting those with limited cyber capabilities. Both countries frame cybersecurity as a domain requiring multistakeholder governance; however, the US tends to frame it within a militarized context, while the UK is more inclined to approach it as a competitive technological field, evident in their distinct uses of WAR and SPORT metaphors. By situating the three metaphors in the linguistic and socio-political contexts where they reside, this study demonstrates that governments use metaphors to legitimize their cybersecurity policies, distinguish in-groups from out-groups, and convey their cyber governance philosophies. This study posits that not only which metaphors, but what discourse contexts they situate in and why they are used, matters when understanding cybersecurity issues in strategy documents.","manuscriptTitle":"Metaphors they strategize by: A corpus-assisted critical metaphor analysis of the US and UK cybersecurity strategies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 18:30:03","doi":"10.21203/rs.3.rs-6632546/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-26T14:04:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T11:31:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201932196968210743559817295297653155617","date":"2025-08-13T20:27:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T20:32:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308986141265833505489934618096620351060","date":"2025-07-27T23:01:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T15:46:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-24T16:59:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-24T13:29:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-24T13:29:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-05-10T05:13:33+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":"4271f31b-9827-44d2-9a02-117faed35274","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51677264,"name":"Humanities/Language and linguistics"},{"id":51677265,"name":"Social science/Language and linguistics"}],"tags":[],"updatedAt":"2026-05-25T02:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 18:30:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6632546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6632546","identity":"rs-6632546","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}