Gender imbalances of retraction prevalence among highly cited authors and among all authors

Gender imbalances of retraction prevalence among highly cited authors and among all authors | 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 Research Article Gender imbalances of retraction prevalence among highly cited authors and among all authors Stefania Boccia, Antonio Cristiano, Angelo Maria Pezzullo, Jeroen Baas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7300359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Scientific retractions remain rare but have become increasingly common. We have previously incorporated retraction data into Scopus-based databases of top-cited (top 2%) scientists to facilitate linkage of retractions with impact metrics at the individual scientist level. Here, we set out to explore whether gender disparities in the likelihood of having retractions exist, both among highly-cited authors and among all authors with ≥ 5 publications. Methods We conducted a descriptive cross-sectional bibliometric analysis of a Scopus-based authors database. We used NamSor to assign gender, retaining only results with a confidence > 85%. We examined the demographics of scientists with and without retractions among highly cited authors (career-long impact: n = 217,097) and among all other authors (n = 10,361,367). We stratified by publication age, field, country income level, and publication volume, and calculated gender-specific retraction rates and the relative propensity (R) of women versus men to have at least one retraction. Results Gender could be classified for 8,267,888 scientists. Among highly cited authors, 3.3% of men and 2.9% of women had at least one retraction; among all authors, the rate was 0.7% for both genders. Differences varied by field: women’s rates were at least one-third lower than men’s (R < 0.67) in Biology, Biomedical Research, and Psychology (R 1.33) in Economics, Engineering, and Information and Communication Technologies. Among highly cited authors, younger cohorts showed increasingly higher rates among men (4.2% men vs. 3.0% women in those starting to publish in 2002–2011; 8.7% men vs. 4.9% women in those starting post-2011). Country-level differences among highly cited authors were pronounced in some countries, as in Pakistan (28.7% men vs. 14.3% women). These differences were smaller among all authors. Conclusion Our analysis shows that gender differences in retraction rates exist but are modest. Field, country, and publication volume are stronger correlates. Structural and contextual factors likely drive retraction patterns and warrant further investigation. Scientific Retractions Gender Disparities Highly Cited Authors Bibliometric Analysis Figures Figure 1 BACKGROUND Gender disparities in science have been noted in various areas, including recruitment, tenure, funding, authorship, and citation impact. While some of these differences may be narrowing over time, the patterns and changes over time differ among scientific disciplines, environments, and countries( 1 ). Citations play a crucial role as academic influence indicators and contributors to inequalities, particularly among the most-cited scientists, impacting academic career trajectories. A previous study reported that among the 2% top-cited authors for each of 174 science subfields (Science-Metrix classification) of a science-wide author database of standardized citation indicators, men outnumbered women by 1.88-fold ( 2 ). Considering 4 publication age cohorts (first publication pre-1992, 1992–2001, 2002–2011, and post-2011), this value decreased from 3.93-fold to 1.36-fold over time.( 2 ) Recently, the 2% top-cited authors database has been expanded to incorporate retraction data ( 3 ). Results show that among 217,097 top-cited scientists in career-long impact and 223,152 in single-year (2023) impact, 7,083 (3.3%) and 8,747 (4.0%), respectively, had at least one retraction. Scientists with retractions had younger publication age, higher self-citation rates, and larger publication volume than those without. No information, however, was available on gender. Notably, in a study examining gender imbalance among retracted biomedical science papers, women comprised 27% of first authors and 24% of last authors, slightly underrepresented compared to estimated general authorship rates of 30–40% for first authors and 25–30% for last authors( 4 ). However, that study did not stratify scientists by publication age cohort or country income level. In this study, we evaluated gender distribution in retractions among highly cited and all authors worldwide with at least 5 publications, using comprehensive publication and citation data from Scopus and Retraction Watch databases, and tested differences across countries and scientific subfields. METHODS We have generated a comprehensive database of the top 2% most-cited scientists in each of the 174 scientific subfields defined by the Science-Metrix classification (RRID:SCR_024471) ( 5 ). This selection was based on a composite citation index, following a methodology similar to our previous studies ( 6 , 7 ). The database also includes scientists who are among the top-100,000 in the composite indicator regardless of their ranking in their primary subfield. The subfields encompass all branches of science, technology, and (bio)medicine, as well as disciplines within the humanities and social sciences. Following a previously established approach ( 3 ), we linked Scopus author entries to the Retraction Watch database (RWDB, RRID:SCR_000654), which is the most reliable database of retractions available to date. This linkage allowed us to track the number of retractions associated with each author ID in the Scopus database. Expressions of concern and corrections without retraction, retraction with republication, and retractions where it is explicit that they are due to publisher/journal error rather than author error were excluded. As in a previous study ( 2 ), we employed NamSor (RRID:SCR_023935)( 8 ), a gender-assignment software, to infer the gender of authors in the Scopus database (RRID:SCR_022559). The NamSor algorithm assigns gender based on an author's first and last name, as well as their country of origin, with a specified confidence level. To determine an author’s country, we used the location of their earliest published paper. We retained only gender assignments with a confidence score above 85%. We focused on the highly cited scientists in the career-long ranking, including self-citations, and compared also to all Scopus authors with ≥ 5 publications. We categorized authors into four cohorts based on the year of their first publication year: Pre-1992 1992–2001 2002–2011 Post-2011. In each age cohort, we classified authors based on their scientific field, using the 20 major fields defined by the Science-Metrix classification( 5 ). We categorized authors by countries of residence, grouping into high-income and other incomes according to the public data from the World Bank ( 9 ). We then calculated the absolute number and proportion of scientists with at least one retracted paper for each gender category. Specifically, we distinguished men with retractions (MR), men without retractions (MWR), women with retractions (WR) and women without retractions (WWR). This analysis was conducted across the four age cohorts, all countries, and all scientific fields. We examined whether the proportion of retracted authors differed by gender within each subgroup and across different scientific domains. We also calculated the relative propensity R of women versus men to have a retraction among top 2% most-cited scientists and among all authors in each subfield. If there are WR + WWR total number of women and MR + MWR total men in a given subfield, and WR and MR of them have at least one retraction, then R = (WR × (MR + MWR)) / (MR × (WR + WWR)) . To explore potential drivers of observed differences in retraction rates, we considered overall publication volume. We stratified authors into tertiles based on their total number of publications, comparing patterns across low, middle, and high publishing groups. We examined the mean and median number of publications in each tertile and across all tertiles and then assessed whether differences in retraction rates varied by publication volume. Data were generated and analyzed centrally at Elsevier Research Intelligence. Since Scopus is a subscription database, the full raw data cannot be shared. Accuracy and precision for Scopus have been presented before ( 6 , 7 , 10 ). We favored presentation of descriptives rather than formal statistical significance testing. Given the large number of authors, statistical significance could have been reached even for minute differences when the entire scientific workforce is considered. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement( 11 ) RESULTS Overall data on authors and retraction rates in men and women Out of 10,361,367 authors with at least five full publications, 8,267,888 could be classified for gender (5,295,929 men and 2,971,959 women), while gender was uncertain for 2,093,479 (20.2%). Among the top 2% most-cited authors, gender was confidently assigned for 186,466 individuals: 155,321 men and 31,145 women, while 61,672 (24.9%) had uncertain gender. These entries were excluded from subsequent analyses. Most authors in the overall database came from high-income countries (6,454,557), while 3,482,436 were from middle- or low-income countries. The remaining authors were affiliated with countries that do not have an income classification from the World Bank, such as small territories, and therefore remained unclassified. Uncertain gender was less frequent in high-income countries (12.7%) than in others (28.8%). Retraction patterns by citation level and gender are summarized in Table 1 . The proportion of authors with at least one retraction was higher among the highly cited scientists (3.3%) than non-highly cited authors (0.7%), a consistent pattern across genders. Among highly cited authors, retraction rates were 2.9% in women and 3.1% in men. Among non-highly cited authors, the rates were identical (0.7%). The rates of authors with retractions were several-fold higher in authors from non-high-income countries than in high-income countries: 7.3% among highly cited authors and 1.4% among non-highly cited, compared to 2.8% and 0.4%, respectively, in high-income countries. After stratifying by income, men had slightly higher retraction rates than women in both country groups. Table 1 Number of authors with at least one retraction across countries of different income levels. Highly cited Non highly cited All citation groups All authors With retraction All authors With retraction All authors With retraction All income levels All Genders 217097 7083 (3.3%) 10144270 72887 (0.7%) 10361367 79970 (0.8%) Women 31145 896 (2.9%) 2940814 19693 (0.7%) 2971959 20589 (0.7%) Men 155321 4752 (3.1%) 5140608 33816 (0.7%) 5295929 38568 (0.7%) High income levels All Genders 193694 5439 (2.8%) 6260863 23030 (0.4%) 6454557 28469 (0.4%) Women 28009 696 (2.5%) 1902994 6559 (0.3%) 1931003 7255 (0.4%) Men 141680 3865 (2.7%) 3558687 13338 (0.4%) 3700367 17203 (0.5%) Other income levels All Genders 22181 1628 (7.3%) 3460255 49645 (1.4%) 3482436 51273 (1.5%) Women 2981 198 (6.6%) 999655 13079 (1.3%) 1002636 13277 (1.3%) Men 12724 876 (6.9%) 1462790 20361 (1.4%) 1475514 21237 (1.4%) Highly cited authors had far more publications than non-highly cited ones, with a median of 163 vs. 11 for all authors regardless of gender (data not shown), and the same pattern across income strata (Supplementary Table 1, Additional File 1). Men had modestly more publications than women (median 14 vs. 11), a pattern also observed across strata. Authors from non-high-income countries who were highly cited had slightly more publications than their high-income counterparts (median 190 vs. 160, data not shown). In non-high-income countries, median publication count was slightly higher in women than men (189 versus 176), whereas the opposite was true in high-income countries (162 vs. 137; Supplementary Table 1, Additional File 1). The majority of authors with at least one retraction (66.2%) were in the first tertile of publication counts (Supplementary Table 2, Additional File 1). Retraction rates in men and women across scientific fields Table 2 presents retraction rates across scientific fields by gender for all authors, and separately for highly cited and not highly cited authors. While gender differences were generally small, some patterns emerged. Excluding fields with fewer than 50 authors with retractions and thus high uncertainty, women had retraction rates at least one-third lower than men (R < 0.67) in Biology, Biomedical Research, and Psychology & Cognitive Sciences. Conversely, in Economics & Business, Engineering, and Information & Communication Technologies, retractions appeared more frequently among women (R > 1.33). R values for non-highly cited authors closely matched those for all authors. Highly cited authors, however, showed different patterns, with highest R values in Mathematics & Statistics (R = 3.06) and Engineering (R = 1.78), indicating higher retraction likelihood among women. In contrast, lower R values among highly cited authors were found in Biomedical Research (R = 0.64), Built Environment & Design (R = 0.65), and Economics & Business (R = 0.68), suggesting relatively lower retraction rates among women. Table 2 Number of women and men with at least one retraction by subfield. Highly Cited R Non Highly Cited R All Authors R Field Women Men Women Men Women Men Agriculture, Fisheries & Forestry 15 (1.4%) 60 (1.1%) 1.19 382 (0.3%) 827 (0.5%) 0.69 397 (0.3%) 887 (0.5%) 0.68 Biology 33 (2.7%) 151 (2.3%) 1.17 567 (0.4%) 1239 (0.7%) 0.67 600 (0.5%) 1390 (0.7%) 0.65 Biomedical Research 92 (3.2%) 609 (5.1%) 0.64 2123 (0.6%) 3087 (0.9%) 0.72 2215 (0.7%) 3,696 (1.0%) 0.65 Built Environment & Design 3 (1.6%) 21 (2.5%) 0.65 40 (0.3%) 111 (0.3%) 0.87 43 (0.3%) 132 (0.4%) 0.79 Chemistry 52 (3.1%) 284 (2.7%) 1.16 850 (0.4%) 1558 (0.4%) 0.95 902 (0.4%) 1842 (0.5%) 0.87 Clinical Medicine 441 (4.0%) 2289 (4.7%) 0.85 10438 (1.0%) 16358 (1.1%) 0.91 10879 (1.0%) 18647 (1.2%) 0.85 Communication & Textual Studies 1 (0.3%) 1 (0.2%) 1.63 12 (0.1%) 23 (0.1%) 0.59 13 (0.1%) 24 (0.1%) 0.62 Earth & Environmental Sciences 16 (2.0%) 91 (1.6%) 1.21 464 (0.5%) 822 (0.5%) 1.11 480 (0.5%) 913 (0.5%) 1.05 Economics & Business 6 (1.0%) 44 (1.4%) 0.68 337 (0.6%) 465 (0.4%) 1.49 343 (0.6%) 509 (0.4%) 1.41 Enabling & Strategic Technologies 71 (3.4%) 384 (3.1%) 1.10 1029 (0.6%) 2240 (0.5%) 1.23 1100 (0.6%) 2624 (0.5%) 1.14 Engineering 52 (3.6%) 247 (2.0%) 1.78 847 (0.6%) 1720 (0.4%) 1.68 899 (0.7%) 1967 (0.4%) 1.58 Historical Studies 0 (0.0%) 0 (0.0%) - 7 (0.0%) 28 (0.1%) 0.45 7 (0.0%) 28 (0.1%) 0.45 Information & Communication Technologies 22 (1.4%) 186 (1.7%) 0.81 1558 (1.1%) 3159 (0.7%) 1.48 1580 (1.1%) 3345 (0.7%) 1.43 Mathematics & Statistics 6 (4.0%) 28 (1.3%) 3.06 94 (0.4%) 308 (0.4%) 0.99 100 (0.4%) 336 (0.4%) 0.99 Philosophy & Theology 0 (0.0%) 0 (0.0%) - 8 (0.1%) 13 (0.1%) 1.79 8 (0.1%) 13 (0.1%) 1.80 Physics & Astronomy 30 (2.3%) 252 (1.6%) 1.43 431 (0.3%) 1326 (0.3%) 1.14 461 (0.3%) 1578 (0.3%) 1.05 Psychology & Cognitive Sciences 24 (2.3%) 65 (2.6%) 0.89 166 (0.3%) 217 (0.4%) 0.68 190 (0.3%) 282 (0.5%) 0.62 Public Health & Health Services 28 (1.6%) 27 (1.6%) 0.96 177 (0.2%) 124 (0.2%) 0.73 205 (0.2%) 151 (0.3%) 0.71 Social Sciences 4 (0.3%) 13 (0.4%) 0.66 162 (0.2%) 191 (0.2%) 1.00 166 (0.2%) 204 (0.2%) 0.97 Visual & Performing Arts 0 (0.0%) 0 (0.0%) - 1 (0.0%) 0 (0.0%) N/A 1 (0.0%) 0 (0.0%) N/A All Fields 896 (2.9%) 4752 (3.1%) 0.94 19693 (0.7%) 33816 (0.7%) 1.02 20589 (0.7%) 38568 (0.7%) 0.95 Retraction rates in men and women across publication age cohorts As shown in Fig. 1 , when different publication age cohorts were considered, men had consistently slightly higher retraction rates than women among all authors (Fig. 1 A), although the difference was negligible in percentage terms. Among highly cited authors, men and women had similar rates in the two older cohorts (Fig. 1 B). However, men had higher rates in the 2002–2011 cohort (4.2% vs. 3.0%), with the difference widening in the youngest cohort (8.7% vs. 4.9%). Supplementary Tables 3–S break down retraction rates by cohort for highly cited, non-highly cited, and all authors. Among highly cited scientists (Supplementary Table 3, Additional File 1), retraction rates for men and women were relatively close in earlier cohorts but diverged across specific fields. In Clinical Medicine, the gender difference widened: women had retraction rates of 3.9% (pre-1992) and 4.6% (1992–2001), dropping to 3.0% (2002–2011) and 0% (post-2011). In contrast, men’s rates rose over time (4.5%, 5.4%, 5.5%, and 6.5%). In Enabling & Strategic Technologies, both genders showed rising retraction rates. Among women, rates increased from 2.6–3.8%, 3.6%, and 6.3%. Men followed a similar pattern: 1.8%, 3.7%, 4.8%, and 8.4%. In Information & Communication Technologies, women had higher rates in the first cohort (2.1%), followed by a decline to 0.8%, 1.6%, and 0%. Men’s rates, by contrast, rose steadily from 0.8–8.9%. In Engineering, women’s rates increased from 2.2–4.6% in the first two cohorts; men’s rose from 1.2–3.5%. Rates were higher in women until the post-2011 cohort, when men surpassed them (3.3% vs. 8.9%). Most other fields had too small numbers of highly cited authors in the youngest (post-2011) cohort to make any meaningful inferences. Among non-highly cited authors (Supplementary Table 4, Additional File 1), retraction rates increased over time for both genders. In Clinical Medicine, where absolute numbers were highest, the gender difference remained roughly constant: men’s rates rose from 0.5–1.2%, women’s from 0.4–1.0%. In Engineering, the gender difference narrowed: in 1992–2001, rates were 0.7% for women and 0.4% for men; in 2002–2011, 1.1% vs. 0.7%; and in post-2011, 0.3% vs. 0.2%. In contrast, Information & Communication Technologies showed a persistent difference with higher rates in women, especially in 2002–2011 (1.8% vs. 1.1%), dissimilar to patterns in the highly cited group. When all fields were combined, there was virtually no gender difference across cohorts among non-highly cited authors. When all authors were combined (Supplementary Table 5, Additional File 1), trends closely mirrored those in the non-highly cited group, with no substantial differences in rates or gender patterns by cohort. Retraction rates in men and women across different countries Among the 40 countries with the highest number of authors (Table 3 ), the highest retraction rates for women among highly cited authors were observed in Pakistan (14.3%), Egypt (13.8%), Iran (9.3%), China (8.1%), India (6.6%), Taiwan (6.5%), Italy (5.7%), and the Czech Republic (5.5%), although in most of these countries absolute numbers were small. The gender difference was particularly notable in Pakistan, where highly cited men had a markedly higher retraction rate than women (28.7% vs. 14.3%). A similar pattern, though attenuated, was observed in Iran (12.4% vs. 9.3%) and India (9.2% vs. 6.6%). Conversely, retraction rates among women were higher than those of men in Italy (5.7% vs. 3.7%), Taiwan (6.5% vs. 4.2%), the Czech Republic (5.5% vs. 3.3%), and Egypt (13.8% vs. 9.0%). In most other countries, gender differences were small. For example, in the United States (2.5% vs. 2.7%), United Kingdom (1.9% vs. 1.9%), and Canada (2.2% vs. 2.6%), rates were nearly identical. Table 3 Number of women and men with at least one retraction by country among the 40 countries with the highest number of authors with ≥ 5 publications Highly Cited Not Highly cited All authors Country Women Men Women Men Women Men United States 340 (2.5%) 1688 (2.7%) 2180 (0.4%) 3946 (0.3%) 2520 (0.4%) 5634 (0.5%) China 148 (8.1%) 325 (7.7%) 10148 (2.1%) 12452 (2.1%) 10296 (2.1%) 12777 (2.2%) Japan 8 (2.3%) 238 (4.1%) 288 (0.3%) 1749 (0.4%) 296 (0.4%) 1987 (0.5%) Germany 33 (3.2%) 259 (2.7%) 307 (0.3%) 796 (0.3%) 340(0.3%) 1055 (0.3%) United Kingdom 60 (1.9%) 284 (1.9%) 300 (0.2%) 644 (0.3%) 360 (0.3%) 928 (0.4%) India 16 (6.6%) 195 (9.2%) 893 (1.1%) 2783 (1.5%) 909 (1.1%) 2978 (1.6%) France 12 (1.3%) 111 (2.2%) 336 (0.3%) 517 (0.3%) 348 (0.3%) 628 (0.4%) Italy 62 (5.7%) 172 (3.7%) 785 (0.6%) 812 (0.5%) 847 (0.7%) 984 (0.6%) Russia 1 (1.4%) 11 (1.3%) 180 (0.3%) 208 (0.2%) 181 (0.3%) 219 (0.2%) Canada 34 (2.2%) 167 (2.6%) 233 (0.3%) 390 (0.3%) 267 (0.3%) 557 (0.4%) Spain 4 (0.7%) 71 (2.8%) 274 (0.3%) 410 (0.4%) 278 (0.3%) 481 (0.4%) South Korea 6 (3.6%) 56 (4.6%) 182 (0.6%) 788 (0.8%) 188 (0.6%) 844 (0.8%) Brazil 4 (2.5%) 18 (2.4%) 205 (0.2%) 276 (0.3%) 209 (0.3%) 294 (0.3%) Australia 27 (2.1%) 124 (2.4%) 189 (0.3%) 315 (0.4%) 216 (0.3%) 439 (0.5%) Netherlands 17 (3.0%) 84 (2.4%) 107 (0.2%) 187 (0.3%) 124 (0.3%) 271 (0.4%) Poland 6 (2.7%) 13 81.6%) 92 (0.2%) 95 (0.2%) 98 (0.2%) 108 (0.2%) Turkey 2 (1.4%) 22 (2.6%) 167 (0.5%) 418 (0.7%) 169 (0.5%) 440 (0.7%) Switzerland 2 (0.5%) 72 (2.7%) 83 (0.3%) 181 (0.3%) 85 (0.3%) 253 (0.4%) Taiwan 14 (6.5%) 27 (4.2%) 187 (0.8%) 258 (0.7%) 201 (0.8%) 285 (0.7%) Sweden 8 (1.7%) 65 (2.6%) 94 (0.3%) 173 (0.3%) 102 (0.3%) 238 (0.5%) Iran 5 (9.3%) 103 (12.4%) 355 (1.2%) 1081 (2.0%) 360 (1.3%) 1184 (2.1%) Belgium 7 (3.0%) 32 (2.1%) 42 (0.2%) 93 (0.2%) 49 (0.2%) 125 (0.3%) Mexico 1 (2.2%) 5 (1.7%) 43 (0.2%) 90 (0.3%) 44 (0.2%) 95 (0.3%) Austria 1 (0.7%) 19 (1.7%) 33 (0.2%) 80 (0.2%) 34 (0.2%) 99 (0.3%) Denmark 6 (2.1%) 37 (2.3%) 35 (0.2%) 86 (0.3%) 41 (0.2%) 123 (0.4%) Indonesia 0 (0.0%) 2 (8.0%) 31 (0.2%) 80 (0.3%) 31 (0.2%) 82 (0.4%) Israel 1 (0.4%) 27 (1.7%) 73 (0.4%) 109 (0.4%) 74 (0.4%) 136 (0.5%) Czech Republic 3 (5.5%) 14 (3.3%) 64 (0.4%) 124 (0.4%) 67 (0.4%) 138 (0.5%) Malaysia 3 (7.1%) 27 (11.1%) 141 (1.0%) 273 (1.1%) 144 (1.0%) 300 (1.2%) Finland 5 (1.6%) 25 (2.6%) 25 (0.1%) 38 (0.2%) 30 (0.2%) 63 (0.2%) Egypt 4 (13.8%) 37 (9.0%) 174 (1.7%) 514 (1.8%) 178 (1.7%) 551(1.9%) Portugal 6 (4.6%) 8 (1.8%) 41 (0.2%) 52 (0.2%) 47 (0.2%) 60 (0.3%) Ukraine 0 (0.0%) 1 (1.2%) 18 (0.1%) 28 (0.2%) 18 (0.1%) 29 (0.2%) Greece 3 (2.6%) 23 (2.7%) 54 (0.4%) 121 (0.5%) 57 (0.4%) 144 (0.5%) Norway 0 (0.0%) 21 (2.0%) 45 (0.3%) 55 (0.2%) 45 (0.3%) 76 (0.3%) Argentina 0 (0.0%) 5 (2.8%) 61 (0.3%) 52 (0.3%) 61 (0.3%) 57 (0.3%) Thailand 0 (0.0%) 6 (4.3%) 52 (0.4%) 63 (0.5%) 52 (0.4%) 69 (0.5%) Pakistan 2 (14.3%) 52 (28.7%) 199 (2.0%) 868 (3.6%) 201 (2.0%) 920 (3.8%) Romania 1 (2.6%) 5 (4.1%) 70 (0.4%) 79 (0.5%) 71 (0.4%) 84 (0.5%) South Africa 5 (3.6%) 8 (1.4%) 43 (0.4%) 56 (0.3%) 48 (0.4%) 64 (0.4%) Among all authors, regardless of citation level, gender differences remained limited. Women had lower retraction rates than men in India (1.1% vs. 1.6%), Pakistan (2.0% vs. 3.8%), and Iran (1.3% vs. 2.1%). In contrast, countries where women had slightly higher rates included Italy (0.7% vs. 0.6%) and South Africa (0.4% vs. 0.3%), though differences were small. In most countries, gender differences remained limited in this broader analysis. Patterns for non-highly cited authors were similar (not shown). DISCUSSION Our analyses show that, overall, retraction rates are slightly lower in women than in men across all authors and among highly cited authors. Similar small gender differences are seen in both high-income and non-high-income countries. However, retraction rates are much higher in non-high-income countries. Upon examining scientific fields separately, most show no major gender differences. Exceptions occur in both directions: some biomedical fields and psychology and cognitive sciences show higher rates for men, while engineering, economics, and information/communication technologies show higher rates for women. When analyzing publication age cohorts, we noted widening differences in younger cohorts, with higher retraction rates in men than women, especially among highly cited authors. Several countries with high retraction rates among highly cited authors also show substantial gender differences, favoring men in some countries and women in others. Retractions may be shaped by a complex set of factors ( 12 – 14 ). Beyond fraudulent or erroneous work, likelihood of retraction may depend on how intensely a paper is scrutinized and whether editorial bias affects decisions. Authors with more publications face a higher chance of retraction. This likely explains why highly cited authors, who publish more, have higher retraction rates than non-highly cited ones, who publish less and draw less scrutiny. Publication volume may also explain most of the small gender difference overall, as women publish slightly fewer papers on average. Higher retraction rates in non-high-income countries may reflect lower quality, higher output among top authors, potential editorial bias or a lower threshold for retracting papers. Fraudulent practices such as paper mills ( 15 – 18 ), cartels ( 19 – 21 ), extreme publishing behavior( 22 ) and precocious citation impact( 23 ) may also be more prevalent in these countries. Local incentive structures could drive these patterns ( 24 , 25 ). Whether such incentives disproportionately affect men is unclear. It may be that such perverse incentives exist more frequently in countries where there is still a large gender difference disfavoring women from publishing and reaching highly cited status. The gender differences observed in some fields may also have complex roots. Fields where women have substantially higher retraction rates (engineering, economics, mathematics, Information & Communication Technologies) tend to be those where women have historically had limited presence, especially among highly cited authors, and where gender differences in authorship remain large ( 2 ). Conversely, fields where women now have lower retraction rates (biology, biomedical science, psychology and cognitive sciences) are those where gender differences have largely disappeared. Bias or gender disadvantages may exist across diverse microenvironments in specific fields. Younger cohorts of highly cited scientists show a substantial gender difference in retraction rates, with much higher rates in men. However, across many fields, data for the post-2011 cohort are too sparse for meaningful interpretation. This difference appears driven by fields such as Clinical Medicine, Biomedical Research, and Information & Communication Technologies. In Clinical Medicine and Biomedical Research, women are now better represented, especially among younger cohorts. In contrast, Information & Communication Technologies remains male-dominated. Its fast pace and competitiveness may increase risks of error or misconduct ( 26 ). Overall, author country appears a much stronger correlate of retractions than gender. Prior work ( 3 , 27 ) highlighted the importance of country in retraction rates, with high retraction rates in countries that have witnessed very massive increases ( 28 , 29 ) in their productivity over the last 20 years, e.g. China, India, Iran, Pakistan, Saudi Arabia, and other non-high income countries. Our gender analysis shows that several of these countries also exhibit higher retraction rates among men, in particular among the group of highly cited authors. Pakistan shows the largest disparity, which persists, though less stark, among non-highly cited authors. Nevertheless, several other countries show the opposite pattern, with higher retraction rates in women than men. This group of countries includes some high-income ones such as Italy, Taiwan and Czech Republic. LIMITATIONS We must acknowledge some limitations of this work. First, a sizeable number of authors could not have their gender assigned confidently and were excluded. This was more common in non-high-income countries. However, gender assignment issues are unlikely to bias the gender–retraction association. Second, some retracted work could not be mapped to Scopus ( 30 ), leading to underestimation of retraction proportions, although this probably also would not affect gender associations. Third, our analysis used large-scale entities (countries, major fields), but retraction patterns may be driven by small teams or microenvironments and occasionally they tend to be clustered. Specific single or few individuals may be primarily responsible for the retractions in such smaller units, but the role of gender, if any, in these occurrences is unclear. Fourth, we did not distinguish between honest error and misconduct. This is precarious to attempt, since retraction notes are often short and vague, and reasons are inconsistently reported ( 31 , 32 ). However, most retractions appear to stem from misconduct ( 33 ). Finally, only some author(s) among many are responsible for a retraction. A recent analysis( 34 ) involving 11,622 retracted and 19,475,437 non-retracted articles across science (Web of Science, 2008–2023) found slightly higher retraction rates (1.23-fold) in male leading authors than female ones, consistent with our findings. CONCLUSIONS Our study provides a comprehensive, science-wide view of gender differences and similarities in retraction rates among highly cited and all authors. Male authors showed slightly higher retraction rates, but these disparities were small and may reflect differences in publication volume and context. On the other hand, fields where women have higher retraction rates tend to be those with historically limited female representation. Overall, gender seems to be a weak modulator of retraction risk, if at all, but may interact with specific fields, countries, or environments, warranting further investigation. Declarations Ethics approval and consent to participate: Not applicable. This study used bibliometric data and did not involve human participants. Consent for publication: Not applicable. Availability of data and materials: The aggregated data and analysis code used in this study are available from the corresponding author on reasonable request. The underlying Scopus data are proprietary to Elsevier; the publicly available database of top-cited scientists used for the sampling frame is available at https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/ (Web of Science Top-Cited Scientists dataset). Competing interests: JB and RG are Elsevier employees. Elsevier runs Scopus, which is the source of these data, and also runs the repository where the database of highly cited scientists is now stored. Funding : The authors received no specific funding for this work. The work of METRICS on retractions is supported by an unrestricted gift from George F. Tidmarsh to Stanford. Authors' contributions: SB conceived the study, drafted the manuscript, and supervised all stages of the project. AC contributed to study design, performed data analyses, and drafted the manuscript. JPAI contributed to study design, interpreted the results, and drafted the manuscript. AMP interpreted the results and provided supervision. JB extracted and provided data and assisted with interpretation. GR extracted and provided data, performed data analyses, and assisted with interpretation. All authors contributed to manuscript revisions and approved the final version. Acknowledgments: This work uses Scopus data provided by Elsevier. References De Kleijn, M, Jayabalasingham, B, Falk-Krzesinski, HJ, Collins, T, Kuiper-Hoyng, L, Cingolani, I, Zhang, J, Roberge, G, et al: The Researcher Journey Through a Gender Lens: An Examination of Research Participation, Career Progression and Perceptions Across the Globe (Elsevier, March 2020). Retrieved from https://www.elsevier.com/insights/gender-and-diversity-in-research/researcher-journey-2020 . Ioannidis JPA, Boyack KW, Collins TA, Baas J. Gender imbalances among top-cited scientists across scientific disciplines over time through the analysis of nearly 5.8 million authors. PLoS Biol. 2023 Nov 21;21(11):e3002385. Ioannidis JPA, Pezzullo AM, Cristiano A, Boccia S, Baas J. Linking citation and retraction data reveals the demographics of scientific retractions among highly cited authors. PLoS Biol. 2025 Jan 30;23(1):e3002999. Pinho-Gomes AC, Hockham C, Woodward M. Women’s representation as authors of retracted papers in the biomedical sciences. PLoS One. 2023 May 3;18(5):e0284403. Archambault É, Beauchesne OH, Caruso J. Towards a Multilingual, Comprehensive and Open Scientific Journal Ontology. In 2013. Available from: https://api.semanticscholar.org/CorpusID:85557623 Ioannidis JPA, Baas J, Klavans R, Boyack KW. A standardized citation metrics author database annotated for scientific field. PLoS Biol. 2019 Aug 12;17(8):e3000384. Ioannidis JPA, Boyack KW, Baas J. Updated science-wide author databases of standardized citation indicators. PLoS Biol. 2020 Oct 16;18(10):e3000918. NamSor. Available from: https://NamSor.app. World Bank. World Bank country and lending groups. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Baas J, Schotten M, Plume A, Côté G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies. 2020 Feb;1(1):377–86. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344–9. Steen RG, Casadevall A, Fang FC. Why has the number of scientific retractions increased? PLoS One. 2013;8(7):e68397. Lievore C, Rubbo P, Dos Santos CB, Picinin CT, Pilatti LA. Research ethics: a profile of retractions from world class universities. Scientometrics. 2021;126(8):6871–89. Li M, Shen Z. Science map of academic misconduct. Innovation (Cambridge (Mass)). 2024 Mar 4;5(2):100593. Candal-Pedreira C, Ross JS, Ruano-Ravina A, Egilman DS, Fernández E, Pérez-Ríos M. Retracted papers originating from paper mills: cross sectional study. BMJ. 2022 Nov 28;e071517. Stone R. In Iran, a shady market for papers flourishes. Science (1979). 2016 Sep 16;353(6305):1197–1197. Abalkina A. Publication and collaboration anomalies in academic papers originating from a paper mill: Evidence from a Russia‐based paper mill. Learned Publishing. 2023 Oct;36(4):689–702. Hvistendahl M. China’s Publication Bazaar. Science (1979). 2013 Nov 29;342(6162):1035–9. Qiu S, Steinwender C, Azoulay P. Paper Tiger? Chinese Science and Home Bias in Citations. Cambridge, MA; 2024 May. Plevris V. From Integrity to Inflation: Ethical and Unethical Citation Practices in Academic Publishing. J Acad Ethics. 2025 Apr 21; Catanzaro M. Citation manipulation found to be rife in math. Science (1979). 2024 Feb 2;383(6682):470–470. Ioannidis JPA, Collins TA, Baas J. Evolving patterns of extreme publishing behavior across science. Scientometrics. 2024 Sep 26;129(9):5783–96. Ioannidis JPA. Features and signals in precocious citation impact: a meta-research study. 2024. Quan W, Chen B, Shu F. Publish or impoverish. Aslib Journal of Information Management. 2017 Sep 18;69(5):486–502. Abritis A, McCook A. Cash incentives for papers go global. Science (1979). 2017 Aug 11;357(6351):541–541. Memon SA, Makovi K, AlShebli B. Characterizing the effect of retractions on publishing careers. Nat Hum Behav. 2025 Jun;9(6):1134–46. Sebo P, Sebo M. Geographical Disparities in Research Misconduct: Analyzing Retraction Patterns by Country. J Med Internet Res. 2025 Jan 14;27:e65775. Thelwall M, Sud P. Scopus 1900–2020: Growth in articles, abstracts, countries, fields, and journals. Quantitative Science Studies. 2022 Apr 12;3(1):37–50. Bornmann L, Wagner C, Leydesdorff L. The geography of references in elite articles: Which countries contribute to the archives of knowledge? PLoS One. 2018 Mar 26;13(3):e0194805. Baas J, Schotten M, Plume A, Côté G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies. 2020 Feb;1(1):377–86. Hwang SY, Yon DK, Lee SW, Kim MS, Kim JY, Smith L, et al. Causes for Retraction in the Biomedical Literature: A Systematic Review of Studies of Retraction Notices. J Korean Med Sci. 2023;38(41). Vuong Q. The limitations of retraction notices and the heroic acts of authors who correct the scholarly record: An analysis of retractions of papers published from 1975 to 2019. Learned Publishing. 2020 Apr 26;33(2):119–30. Fang FC, Steen RG, Casadevall A. Misconduct accounts for the majority of retracted scientific publications. Proceedings of the National Academy of Sciences. 2012 Oct 16;109(42):17028–33. Zheng ET, Fu HZ, Thelwall M, Fang Z. Do male leading authors retract more articles than female leading authors? J Informetr. 2025 Aug;19(3):101682. Supplementary Files additionalfilesbmc.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 02 Jan, 2026 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 06 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7300359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497063230,"identity":"f1fcf128-fde9-434a-9526-f5f7560dd767","order_by":0,"name":"Stefania Boccia","email":"","orcid":"","institution":"Universita Cattolica del Sacro Cuore Facolta di Medicina e Chirurgia","correspondingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Boccia","suffix":""},{"id":497063231,"identity":"b0bc9bd7-398f-4dbe-a236-423616257c0d","order_by":1,"name":"Antonio Cristiano","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-7994-5230","institution":"Universita Cattolica del Sacro Cuore Facolta di Medicina e Chirurgia","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Cristiano","suffix":""},{"id":497063232,"identity":"19a16970-d959-48c7-b93d-3322a1ea84a3","order_by":2,"name":"Angelo Maria Pezzullo","email":"","orcid":"","institution":"Universita Cattolica del Sacro Cuore Facolta di Medicina e Chirurgia","correspondingAuthor":false,"prefix":"","firstName":"Angelo","middleName":"Maria","lastName":"Pezzullo","suffix":""},{"id":497063233,"identity":"07dba4f1-a43b-49c3-bdca-faeddc73af5b","order_by":3,"name":"Jeroen Baas","email":"","orcid":"","institution":"Elsevier BV","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"","lastName":"Baas","suffix":""},{"id":497063234,"identity":"aa3a2f62-9415-4d88-bda7-742a92845fd8","order_by":4,"name":"Guillaume Roberge","email":"","orcid":"","institution":"Elsevier BV","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Roberge","suffix":""},{"id":497063235,"identity":"f7899df4-4875-46e6-954c-c91d9a2bf5cb","order_by":5,"name":"John P.A. Ioannidis","email":"","orcid":"","institution":"Stanford University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"P.A.","lastName":"Ioannidis","suffix":""}],"badges":[],"createdAt":"2025-08-05 12:04:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7300359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7300359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88922463,"identity":"c3b0705d-7e02-4004-a5b3-f41c50ccca72","added_by":"auto","created_at":"2025-08-12 17:50:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":392275,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7300359/v1/d110dafbda1437024b6a4731.png"},{"id":88923289,"identity":"b05432d1-1a1e-46c5-8ffc-06ba6a228553","added_by":"auto","created_at":"2025-08-12 18:06:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7300359/v1/7a8dc6e6-7648-4778-8820-16bc680258b9.pdf"},{"id":88923017,"identity":"4f40d3be-be6c-4a4a-a262-4d449b879e2d","added_by":"auto","created_at":"2025-08-12 17:58:27","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":57633,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfilesbmc.docx","url":"https://assets-eu.researchsquare.com/files/rs-7300359/v1/eb18a660d2482430f6ed3bc1.docx"}],"financialInterests":"","formattedTitle":"Gender imbalances of retraction prevalence among highly cited authors and among all authors","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eGender disparities in science have been noted in various areas, including recruitment, tenure, funding, authorship, and citation impact. While some of these differences may be narrowing over time, the patterns and changes over time differ among scientific disciplines, environments, and countries(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Citations play a crucial role as academic influence indicators and contributors to inequalities, particularly among the most-cited scientists, impacting academic career trajectories. A previous study reported that among the 2% top-cited authors for each of 174 science subfields (Science-Metrix classification) of a science-wide author database of standardized citation indicators, men outnumbered women by 1.88-fold (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Considering 4 publication age cohorts (first publication pre-1992, 1992–2001, 2002–2011, and post-2011), this value decreased from 3.93-fold to 1.36-fold over time.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eRecently, the 2% top-cited authors database has been expanded to incorporate retraction data (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Results show that among 217,097 top-cited scientists in career-long impact and 223,152 in single-year (2023) impact, 7,083 (3.3%) and 8,747 (4.0%), respectively, had at least one retraction. Scientists with retractions had younger publication age, higher self-citation rates, and larger publication volume than those without. No information, however, was available on gender. Notably, in a study examining gender imbalance among retracted biomedical science papers, women comprised 27% of first authors and 24% of last authors, slightly underrepresented compared to estimated general authorship rates of 30–40% for first authors and 25–30% for last authors(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, that study did not stratify scientists by publication age cohort or country income level.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated gender distribution in retractions among highly cited and all authors worldwide with at least 5 publications, using comprehensive publication and citation data from Scopus and Retraction Watch databases, and tested differences across countries and scientific subfields.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe have generated a comprehensive database of the top 2% most-cited scientists in each of the 174 scientific subfields defined by the Science-Metrix classification (RRID:SCR_024471) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This selection was based on a composite citation index, following a methodology similar to our previous studies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The database also includes scientists who are among the top-100,000 in the composite indicator regardless of their ranking in their primary subfield. The subfields encompass all branches of science, technology, and (bio)medicine, as well as disciplines within the humanities and social sciences. Following a previously established approach (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), we linked Scopus author entries to the Retraction Watch database (RWDB, RRID:SCR_000654), which is the most reliable database of retractions available to date. This linkage allowed us to track the number of retractions associated with each author ID in the Scopus database. Expressions of concern and corrections without retraction, retraction with republication, and retractions where it is explicit that they are due to publisher/journal error rather than author error were excluded.\u003c/p\u003e\u003cp\u003eAs in a previous study (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), we employed NamSor (RRID:SCR_023935)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), a gender-assignment software, to infer the gender of authors in the Scopus database (RRID:SCR_022559). The NamSor algorithm assigns gender based on an author's first and last name, as well as their country of origin, with a specified confidence level. To determine an author’s country, we used the location of their earliest published paper. We retained only gender assignments with a confidence score above 85%.\u003c/p\u003e\u003cp\u003eWe focused on the highly cited scientists in the career-long ranking, including self-citations, and compared also to all Scopus authors with ≥ 5 publications. We categorized authors into four cohorts based on the year of their first publication year:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePre-1992\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e1992–2001\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e2002–2011\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePost-2011.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIn each age cohort, we classified authors based on their scientific field, using the 20 major fields defined by the Science-Metrix classification(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). We categorized authors by countries of residence, grouping into high-income and other incomes according to the public data from the World Bank (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We then calculated the absolute number and proportion of scientists with at least one retracted paper for each gender category. Specifically, we distinguished men with retractions (MR), men without retractions (MWR), women with retractions (WR) and women without retractions (WWR). This analysis was conducted across the four age cohorts, all countries, and all scientific fields.\u003c/p\u003e\u003cp\u003eWe examined whether the proportion of retracted authors differed by gender within each subgroup and across different scientific domains. We also calculated the relative propensity R of women versus men to have a retraction among top 2% most-cited scientists and among all authors in each subfield. If there are WR + WWR total number of women and MR + MWR total men in a given subfield, and WR and MR of them have at least one retraction, then \u003cb\u003eR = (WR × (MR + MWR)) / (MR × (WR + WWR))\u003c/b\u003e. To explore potential drivers of observed differences in retraction rates, we considered overall publication volume. We stratified authors into tertiles based on their total number of publications, comparing patterns across low, middle, and high publishing groups. We examined the mean and median number of publications in each tertile and across all tertiles and then assessed whether differences in retraction rates varied by publication volume.\u003c/p\u003e\u003cp\u003eData were generated and analyzed centrally at Elsevier Research Intelligence. Since Scopus is a subscription database, the full raw data cannot be shared. Accuracy and precision for Scopus have been presented before (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). We favored presentation of descriptives rather than formal statistical significance testing. Given the large number of authors, statistical significance could have been reached even for minute differences when the entire scientific workforce is considered. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eOverall data on authors and retraction rates in men and women\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 10,361,367 authors with at least five full publications, 8,267,888 could be classified for gender (5,295,929 men and 2,971,959 women), while gender was uncertain for 2,093,479 (20.2%). Among the top 2% most-cited authors, gender was confidently assigned for 186,466 individuals: 155,321 men and 31,145 women, while 61,672 (24.9%) had uncertain gender. These entries were excluded from subsequent analyses. Most authors in the overall database came from high-income countries (6,454,557), while 3,482,436 were from middle- or low-income countries. The remaining authors were affiliated with countries that do not have an income classification from the World Bank, such as small territories, and therefore remained unclassified. Uncertain gender was less frequent in high-income countries (12.7%) than in others (28.8%).\u003c/p\u003e\n\u003cp\u003eRetraction patterns by citation level and gender are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The proportion of authors with at least one retraction was higher among the highly cited scientists (3.3%) than non-highly cited authors (0.7%), a consistent pattern across genders. Among highly cited authors, retraction rates were 2.9% in women and 3.1% in men. Among non-highly cited authors, the rates were identical (0.7%). The rates of authors with retractions were several-fold higher in authors from non-high-income countries than in high-income countries: 7.3% among highly cited authors and 1.4% among non-highly cited, compared to 2.8% and 0.4%, respectively, in high-income countries. After stratifying by income, men had slightly higher retraction rates than women in both country groups.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of authors with at least one retraction across countries of different income levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHighly cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon highly cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll citation groups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith retraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith retraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith retraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll income levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Genders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7083 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10144270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72887 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10361367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79970 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e896 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2940814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19693 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2971959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20589 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4752 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5140608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33816 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5295929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38568 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh income levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Genders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5439 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6260863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23030 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6454557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28469 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e696 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1902994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6559 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1931003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7255 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3865 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3558687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13338 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3700367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17203 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther income levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Genders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1628 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3460255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49645 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3482436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51273 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e999655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13079 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1002636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13277 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1462790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20361 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1475514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21237 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eHighly cited authors had far more publications than non-highly cited ones, with a median of 163 vs. 11 for all authors regardless of gender (data not shown), and the same pattern across income strata (Supplementary Table 1, Additional File 1). Men had modestly more publications than women (median 14 vs. 11), a pattern also observed across strata. Authors from non-high-income countries who were highly cited had slightly more publications than their high-income counterparts (median 190 vs. 160, data not shown). In non-high-income countries, median publication count was slightly higher in women than men (189 versus 176), whereas the opposite was true in high-income countries (162 vs. 137; Supplementary Table 1, Additional File 1). The majority of authors with at least one retraction (66.2%) were in the first tertile of publication counts (Supplementary Table 2, Additional File 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetraction rates in men and women across scientific fields\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents retraction rates across scientific fields by gender for all authors, and separately for highly cited and not highly cited authors. While gender differences were generally small, some patterns emerged. Excluding fields with fewer than 50 authors with retractions and thus high uncertainty, women had retraction rates at least one-third lower than men (R\u0026thinsp;\u0026lt;\u0026thinsp;0.67) in Biology, Biomedical Research, and Psychology \u0026amp; Cognitive Sciences. Conversely, in Economics \u0026amp; Business, Engineering, and Information \u0026amp; Communication Technologies, retractions appeared more frequently among women (R\u0026thinsp;\u0026gt;\u0026thinsp;1.33). R values for non-highly cited authors closely matched those for all authors. Highly cited authors, however, showed different patterns, with highest R values in Mathematics \u0026amp; Statistics (R\u0026thinsp;=\u0026thinsp;3.06) and Engineering (R\u0026thinsp;=\u0026thinsp;1.78), indicating higher retraction likelihood among women. In contrast, lower R values among highly cited authors were found in Biomedical Research (R\u0026thinsp;=\u0026thinsp;0.64), Built Environment \u0026amp; Design (R\u0026thinsp;=\u0026thinsp;0.65), and Economics \u0026amp; Business (R\u0026thinsp;=\u0026thinsp;0.68), suggesting relatively lower retraction rates among women.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of women and men with at least one retraction by subfield.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHighly Cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon Highly Cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll Authors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eField\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture, Fisheries \u0026amp; Forestry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e827 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e887 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1239 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1390 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiomedical Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2123 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3087 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2215 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,696 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Environment \u0026amp; Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e850 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1558 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e902 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1842 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2289 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10438 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16358 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10879 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18647 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunication \u0026amp; Textual Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarth \u0026amp; Environmental Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e464 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e822 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomics \u0026amp; Business\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e509 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnabling \u0026amp; Strategic Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1029 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2240 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1100 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2624 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e847 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1720 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e899 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1967 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistorical Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation \u0026amp; Communication Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1558 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3159 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1580 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3345 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMathematics \u0026amp; Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhilosophy \u0026amp; Theology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysics \u0026amp; Astronomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1326 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e461 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1578 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychology \u0026amp; Cognitive Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Health \u0026amp; Health Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisual \u0026amp; Performing Arts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e896 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4752 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19693 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33816 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20589 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38568 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetraction rates in men and women across publication age cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, when different publication age cohorts were considered, men had consistently slightly higher retraction rates than women among all authors (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), although the difference was negligible in percentage terms. Among highly cited authors, men and women had similar rates in the two older cohorts (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, men had higher rates in the 2002\u0026ndash;2011 cohort (4.2% vs. 3.0%), with the difference widening in the youngest cohort (8.7% vs. 4.9%). Supplementary Tables 3\u0026ndash;S break down retraction rates by cohort for highly cited, non-highly cited, and all authors. Among highly cited scientists (Supplementary Table 3, Additional File 1), retraction rates for men and women were relatively close in earlier cohorts but diverged across specific fields. In Clinical Medicine, the gender difference widened: women had retraction rates of 3.9% (pre-1992) and 4.6% (1992\u0026ndash;2001), dropping to 3.0% (2002\u0026ndash;2011) and 0% (post-2011). In contrast, men\u0026rsquo;s rates rose over time (4.5%, 5.4%, 5.5%, and 6.5%). In Enabling \u0026amp; Strategic Technologies, both genders showed rising retraction rates. Among women, rates increased from 2.6\u0026ndash;3.8%, 3.6%, and 6.3%. Men followed a similar pattern: 1.8%, 3.7%, 4.8%, and 8.4%. In Information \u0026amp; Communication Technologies, women had higher rates in the first cohort (2.1%), followed by a decline to 0.8%, 1.6%, and 0%. Men\u0026rsquo;s rates, by contrast, rose steadily from 0.8\u0026ndash;8.9%. In Engineering, women\u0026rsquo;s rates increased from 2.2\u0026ndash;4.6% in the first two cohorts; men\u0026rsquo;s rose from 1.2\u0026ndash;3.5%. Rates were higher in women until the post-2011 cohort, when men surpassed them (3.3% vs. 8.9%). Most other fields had too small numbers of highly cited authors in the youngest (post-2011) cohort to make any meaningful inferences.\u003c/p\u003e\n\u003cp\u003eAmong non-highly cited authors (Supplementary Table 4, Additional File 1), retraction rates increased over time for both genders. In Clinical Medicine, where absolute numbers were highest, the gender difference remained roughly constant: men\u0026rsquo;s rates rose from 0.5\u0026ndash;1.2%, women\u0026rsquo;s from 0.4\u0026ndash;1.0%. In Engineering, the gender difference narrowed: in 1992\u0026ndash;2001, rates were 0.7% for women and 0.4% for men; in 2002\u0026ndash;2011, 1.1% vs. 0.7%; and in post-2011, 0.3% vs. 0.2%. In contrast, Information \u0026amp; Communication Technologies showed a persistent difference with higher rates in women, especially in 2002\u0026ndash;2011 (1.8% vs. 1.1%), dissimilar to patterns in the highly cited group. When all fields were combined, there was virtually no gender difference across cohorts among non-highly cited authors.\u003c/p\u003e\n\u003cp\u003eWhen all authors were combined (Supplementary Table\u0026nbsp;5, Additional File 1), trends closely mirrored those in the non-highly cited group, with no substantial differences in rates or gender patterns by cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetraction rates in men and women across different countries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 40 countries with the highest number of authors (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the highest retraction rates for women among highly cited authors were observed in Pakistan (14.3%), Egypt (13.8%), Iran (9.3%), China (8.1%), India (6.6%), Taiwan (6.5%), Italy (5.7%), and the Czech Republic (5.5%), although in most of these countries absolute numbers were small. The gender difference was particularly notable in Pakistan, where highly cited men had a markedly higher retraction rate than women (28.7% vs. 14.3%). A similar pattern, though attenuated, was observed in Iran (12.4% vs. 9.3%) and India (9.2% vs. 6.6%). Conversely, retraction rates among women were higher than those of men in Italy (5.7% vs. 3.7%), Taiwan (6.5% vs. 4.2%), the Czech Republic (5.5% vs. 3.3%), and Egypt (13.8% vs. 9.0%). In most other countries, gender differences were small. For example, in the United States (2.5% vs. 2.7%), United Kingdom (1.9% vs. 1.9%), and Canada (2.2% vs. 2.6%), rates were nearly identical.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of women and men with at least one retraction by country among the 40 countries with the highest number of authors with \u0026ge;\u0026thinsp;5 publications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHighly Cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNot Highly cited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll authors\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1688 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2180 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3946 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2520 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5634 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10148 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12452 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10296 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12777 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1749 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1987 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e796 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1055 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e644 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e928 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e893 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2783 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e909 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2978 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e785 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e812 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e847 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e788 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 81.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1081 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1184 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCzech Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e514 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e551(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArgentina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRomania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eAmong all authors, regardless of citation level, gender differences remained limited. Women had lower retraction rates than men in India (1.1% vs. 1.6%), Pakistan (2.0% vs. 3.8%), and Iran (1.3% vs. 2.1%). In contrast, countries where women had slightly higher rates included Italy (0.7% vs. 0.6%) and South Africa (0.4% vs. 0.3%), though differences were small. In most countries, gender differences remained limited in this broader analysis. Patterns for non-highly cited authors were similar (not shown).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur analyses show that, overall, retraction rates are slightly lower in women than in men across all authors and among highly cited authors. Similar small gender differences are seen in both high-income and non-high-income countries. However, retraction rates are much higher in non-high-income countries. Upon examining scientific fields separately, most show no major gender differences. Exceptions occur in both directions: some biomedical fields and psychology and cognitive sciences show higher rates for men, while engineering, economics, and information/communication technologies show higher rates for women. When analyzing publication age cohorts, we noted widening differences in younger cohorts, with higher retraction rates in men than women, especially among highly cited authors. Several countries with high retraction rates among highly cited authors also show substantial gender differences, favoring men in some countries and women in others.\u003c/p\u003e\u003cp\u003eRetractions may be shaped by a complex set of factors (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Beyond fraudulent or erroneous work, likelihood of retraction may depend on how intensely a paper is scrutinized and whether editorial bias affects decisions. Authors with more publications face a higher chance of retraction. This likely explains why highly cited authors, who publish more, have higher retraction rates than non-highly cited ones, who publish less and draw less scrutiny. Publication volume may also explain most of the small gender difference overall, as women publish slightly fewer papers on average. Higher retraction rates in non-high-income countries may reflect lower quality, higher output among top authors, potential editorial bias or a lower threshold for retracting papers. Fraudulent practices such as paper mills (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), cartels (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), extreme publishing behavior(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and precocious citation impact(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) may also be more prevalent in these countries. Local incentive structures could drive these patterns (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Whether such incentives disproportionately affect men is unclear. It may be that such perverse incentives exist more frequently in countries where there is still a large gender difference disfavoring women from publishing and reaching highly cited status.\u003c/p\u003e\u003cp\u003eThe gender differences observed in some fields may also have complex roots. Fields where women have substantially higher retraction rates (engineering, economics, mathematics, Information \u0026amp; Communication Technologies) tend to be those where women have historically had limited presence, especially among highly cited authors, and where gender differences in authorship remain large (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Conversely, fields where women now have lower retraction rates (biology, biomedical science, psychology and cognitive sciences) are those where gender differences have largely disappeared. Bias or gender disadvantages may exist across diverse microenvironments in specific fields.\u003c/p\u003e\u003cp\u003eYounger cohorts of highly cited scientists show a substantial gender difference in retraction rates, with much higher rates in men. However, across many fields, data for the post-2011 cohort are too sparse for meaningful interpretation. This difference appears driven by fields such as Clinical Medicine, Biomedical Research, and Information \u0026amp; Communication Technologies. In Clinical Medicine and Biomedical Research, women are now better represented, especially among younger cohorts. In contrast, Information \u0026amp; Communication Technologies remains male-dominated. Its fast pace and competitiveness may increase risks of error or misconduct (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, author country appears a much stronger correlate of retractions than gender. Prior work (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) highlighted the importance of country in retraction rates, with high retraction rates in countries that have witnessed very massive increases (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) in their productivity over the last 20 years, e.g. China, India, Iran, Pakistan, Saudi Arabia, and other non-high income countries. Our gender analysis shows that several of these countries also exhibit higher retraction rates among men, in particular among the group of highly cited authors. Pakistan shows the largest disparity, which persists, though less stark, among non-highly cited authors. Nevertheless, several other countries show the opposite pattern, with higher retraction rates in women than men. This group of countries includes some high-income ones such as Italy, Taiwan and Czech Republic.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLIMITATIONS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe must acknowledge some limitations of this work. First, a sizeable number of authors could not have their gender assigned confidently and were excluded. This was more common in non-high-income countries. However, gender assignment issues are unlikely to bias the gender\u0026ndash;retraction association. Second, some retracted work could not be mapped to Scopus (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), leading to underestimation of retraction proportions, although this probably also would not affect gender associations. Third, our analysis used large-scale entities (countries, major fields), but retraction patterns may be driven by small teams or microenvironments and occasionally they tend to be clustered. Specific single or few individuals may be primarily responsible for the retractions in such smaller units, but the role of gender, if any, in these occurrences is unclear. Fourth, we did not distinguish between honest error and misconduct. This is precarious to attempt, since retraction notes are often short and vague, and reasons are inconsistently reported (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, most retractions appear to stem from misconduct (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Finally, only some author(s) among many are responsible for a retraction. A recent analysis(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) involving 11,622 retracted and 19,475,437 non-retracted articles across science (Web of Science, 2008\u0026ndash;2023) found slightly higher retraction rates (1.23-fold) in male leading authors than female ones, consistent with our findings.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur study provides a comprehensive, science-wide view of gender differences and similarities in retraction rates among highly cited and all authors. Male authors showed slightly higher retraction rates, but these disparities were small and may reflect differences in publication volume and context. On the other hand, fields where women have higher retraction rates tend to be those with historically limited female representation. Overall, gender seems to be a weak modulator of retraction risk, if at all, but may interact with specific fields, countries, or environments, warranting further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study used bibliometric data and did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aggregated data and analysis code used in this study are available from the corresponding author on reasonable request. The underlying Scopus data are proprietary to Elsevier; the publicly available database of top-cited scientists used for the sampling frame is available at https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/ (Web of Science Top-Cited Scientists dataset).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJB and RG are Elsevier employees. Elsevier runs Scopus, which is the source of these data, and also runs the repository where the database of highly cited scientists is now stored.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work. \u0026nbsp;The work of METRICS on retractions is supported by an unrestricted gift from George F. Tidmarsh to Stanford.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;SB conceived the study, drafted the manuscript, and supervised all stages of the project. AC contributed to study design, performed data analyses, and drafted the manuscript. JPAI contributed to study design, interpreted the results, and drafted the manuscript. AMP interpreted the results and provided supervision. JB extracted and provided data and assisted with interpretation. GR extracted and provided data, performed data analyses, and assisted with interpretation. All authors contributed to manuscript revisions and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work uses Scopus data provided by Elsevier.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDe Kleijn, M, Jayabalasingham, B, Falk-Krzesinski, HJ, Collins, T, Kuiper-Hoyng, L, Cingolani, I, Zhang, J, Roberge, G, et al: The Researcher Journey Through a Gender Lens: An Examination of Research Participation, Career Progression and Perceptions Across the Globe (Elsevier, March 2020). Retrieved from https://www.elsevier.com/insights/gender-and-diversity-in-research/researcher-journey-2020 . \u003c/li\u003e\n\u003cli\u003eIoannidis JPA, Boyack KW, Collins TA, Baas J. Gender imbalances among top-cited scientists across scientific disciplines over time through the analysis of nearly 5.8 million authors. PLoS Biol. 2023 Nov 21;21(11):e3002385. \u003c/li\u003e\n\u003cli\u003eIoannidis JPA, Pezzullo AM, Cristiano A, Boccia S, Baas J. Linking citation and retraction data reveals the demographics of scientific retractions among highly cited authors. PLoS Biol. 2025 Jan 30;23(1):e3002999. \u003c/li\u003e\n\u003cli\u003ePinho-Gomes AC, Hockham C, Woodward M. Women\u0026rsquo;s representation as authors of retracted papers in the biomedical sciences. PLoS One. 2023 May 3;18(5):e0284403. \u003c/li\u003e\n\u003cli\u003eArchambault \u0026Eacute;, Beauchesne OH, Caruso J. Towards a Multilingual, Comprehensive and Open Scientific Journal Ontology. In 2013. Available from: https://api.semanticscholar.org/CorpusID:85557623\u003c/li\u003e\n\u003cli\u003eIoannidis JPA, Baas J, Klavans R, Boyack KW. A standardized citation metrics author database annotated for scientific field. PLoS Biol. 2019 Aug 12;17(8):e3000384. \u003c/li\u003e\n\u003cli\u003eIoannidis JPA, Boyack KW, Baas J. Updated science-wide author databases of standardized citation indicators. PLoS Biol. 2020 Oct 16;18(10):e3000918. \u003c/li\u003e\n\u003cli\u003eNamSor. Available from: https://NamSor.app. \u003c/li\u003e\n\u003cli\u003eWorld Bank. World Bank country and lending groups. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. \u003c/li\u003e\n\u003cli\u003eBaas J, Schotten M, Plume A, C\u0026ocirc;t\u0026eacute; G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies. 2020 Feb;1(1):377\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSteen RG, Casadevall A, Fang FC. Why has the number of scientific retractions increased? PLoS One. 2013;8(7):e68397. \u003c/li\u003e\n\u003cli\u003eLievore C, Rubbo P, Dos Santos CB, Picinin CT, Pilatti LA. Research ethics: a profile of retractions from world class universities. Scientometrics. 2021;126(8):6871\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eLi M, Shen Z. Science map of academic misconduct. Innovation (Cambridge (Mass)). 2024 Mar 4;5(2):100593. \u003c/li\u003e\n\u003cli\u003eCandal-Pedreira C, Ross JS, Ruano-Ravina A, Egilman DS, Fern\u0026aacute;ndez E, P\u0026eacute;rez-R\u0026iacute;os M. Retracted papers originating from paper mills: cross sectional study. BMJ. 2022 Nov 28;e071517. \u003c/li\u003e\n\u003cli\u003eStone R. In Iran, a shady market for papers flourishes. Science (1979). 2016 Sep 16;353(6305):1197\u0026ndash;1197. \u003c/li\u003e\n\u003cli\u003eAbalkina A. Publication and collaboration anomalies in academic papers originating from a paper mill: Evidence from a Russia‐based paper mill. Learned Publishing. 2023 Oct;36(4):689\u0026ndash;702. \u003c/li\u003e\n\u003cli\u003eHvistendahl M. China\u0026rsquo;s Publication Bazaar. Science (1979). 2013 Nov 29;342(6162):1035\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eQiu S, Steinwender C, Azoulay P. Paper Tiger? Chinese Science and Home Bias in Citations. Cambridge, MA; 2024 May. \u003c/li\u003e\n\u003cli\u003ePlevris V. From Integrity to Inflation: Ethical and Unethical Citation Practices in Academic Publishing. J Acad Ethics. 2025 Apr 21; \u003c/li\u003e\n\u003cli\u003eCatanzaro M. Citation manipulation found to be rife in math. Science (1979). 2024 Feb 2;383(6682):470\u0026ndash;470. \u003c/li\u003e\n\u003cli\u003eIoannidis JPA, Collins TA, Baas J. Evolving patterns of extreme publishing behavior across science. 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Quantitative Science Studies. 2022 Apr 12;3(1):37\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eBornmann L, Wagner C, Leydesdorff L. The geography of references in elite articles: Which countries contribute to the archives of knowledge? PLoS One. 2018 Mar 26;13(3):e0194805. \u003c/li\u003e\n\u003cli\u003eBaas J, Schotten M, Plume A, C\u0026ocirc;t\u0026eacute; G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies. 2020 Feb;1(1):377\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eHwang SY, Yon DK, Lee SW, Kim MS, Kim JY, Smith L, et al. Causes for Retraction in the Biomedical Literature: A Systematic Review of Studies of Retraction Notices. J Korean Med Sci. 2023;38(41). \u003c/li\u003e\n\u003cli\u003eVuong Q. The limitations of retraction notices and the heroic acts of authors who correct the scholarly record: An analysis of retractions of papers published from 1975 to 2019. Learned Publishing. 2020 Apr 26;33(2):119\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eFang FC, Steen RG, Casadevall A. Misconduct accounts for the majority of retracted scientific publications. Proceedings of the National Academy of Sciences. 2012 Oct 16;109(42):17028\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eZheng ET, Fu HZ, Thelwall M, Fang Z. Do male leading authors retract more articles than female leading authors? J Informetr. 2025 Aug;19(3):101682. \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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"research-integrity-and-peer-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ripr","sideBox":"Learn more about [Research Integrity and Peer Review](http://researchintegrityjournal.biomedcentral.com)","snPcode":"41073","submissionUrl":"https://submission.nature.com/new-submission/41073/3","title":"Research Integrity and Peer Review","twitterHandle":"@RIPRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Scientific Retractions, Gender Disparities, Highly Cited Authors, Bibliometric Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7300359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7300359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eScientific retractions remain rare but have become increasingly common. We have previously incorporated retraction data into Scopus-based databases of top-cited (top 2%) scientists to facilitate linkage of retractions with impact metrics at the individual scientist level. Here, we set out to explore whether gender disparities in the likelihood of having retractions exist, both among highly-cited authors and among all authors with \u0026ge;\u0026thinsp;5 publications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a descriptive cross-sectional bibliometric analysis of a Scopus-based authors database. We used NamSor to assign gender, retaining only results with a confidence\u0026thinsp;\u0026gt;\u0026thinsp;85%. We examined the demographics of scientists with and without retractions among highly cited authors (career-long impact: n\u0026thinsp;=\u0026thinsp;217,097) and among all other authors (n\u0026thinsp;=\u0026thinsp;10,361,367). We stratified by publication age, field, country income level, and publication volume, and calculated gender-specific retraction rates and the relative propensity (R) of women versus men to have at least one retraction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGender could be classified for 8,267,888 scientists. Among highly cited authors, 3.3% of men and 2.9% of women had at least one retraction; among all authors, the rate was 0.7% for both genders. Differences varied by field: women\u0026rsquo;s rates were at least one-third lower than men\u0026rsquo;s (R\u0026thinsp;\u0026lt;\u0026thinsp;0.67) in Biology, Biomedical Research, and Psychology (R\u0026thinsp;\u0026lt;\u0026thinsp;0.67), but higher (R\u0026thinsp;\u0026gt;\u0026thinsp;1.33) in Economics, Engineering, and Information and Communication Technologies. Among highly cited authors, younger cohorts showed increasingly higher rates among men (4.2% men vs. 3.0% women in those starting to publish in 2002\u0026ndash;2011; 8.7% men vs. 4.9% women in those starting post-2011). Country-level differences among highly cited authors were pronounced in some countries, as in Pakistan (28.7% men vs. 14.3% women). These differences were smaller among all authors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur analysis shows that gender differences in retraction rates exist but are modest. Field, country, and publication volume are stronger correlates. Structural and contextual factors likely drive retraction patterns and warrant further investigation.\u003c/p\u003e","manuscriptTitle":"Gender imbalances of retraction prevalence among highly cited authors and among all authors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 17:50:23","doi":"10.21203/rs.3.rs-7300359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2026-01-02T12:31:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-07T09:55:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T09:25:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T11:10:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Research Integrity and Peer Review","date":"2025-08-05T11:41:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"research-integrity-and-peer-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ripr","sideBox":"Learn more about [Research Integrity and Peer Review](http://researchintegrityjournal.biomedcentral.com)","snPcode":"41073","submissionUrl":"https://submission.nature.com/new-submission/41073/3","title":"Research Integrity and Peer Review","twitterHandle":"@RIPRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e48d13d8-d03e-4851-a649-e5fb023a85f3","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T11:17:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 17:50:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7300359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7300359","identity":"rs-7300359","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}