{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:44:18Z","timestamp":1784936658567,"version":"3.55.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["10039436"],"award-info":[{"award-number":["10039436"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["10039436"],"award-info":[{"award-number":["10039436"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["10039436"],"award-info":[{"award-number":["10039436"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["10039436"],"award-info":[{"award-number":["10039436"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["10039436"],"award-info":[{"award-number":["10039436"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Panteion University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Qual Quant"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The interplay between technology and societal norms often reveals a troubling reality: machine learning systems not only reflect existing gender stereotypes but can also amplify and entrench them, making these biases harder to detect and address. This paper adopts an interdisciplinary approach, combining quantitative and qualitative methods with recent technological advancements, such as machine learning techniques for textual analysis and computational linguistics, to offer a new framework for understanding occupational gender bias in machine learning. The study is motivated by persistent gender inequalities in the labour market and rising concerns about gendered algorithmic bias, as outlined in the European Commission\u2019s Gender Equality Strategy 2020\u20132025. Focusing on language translation technologies, the research explores how machine learning may perpetuate or amplify gender stereotypes, aiming to foster more inclusive digital systems aligned with EU strategic goals. More specifically, it investigates occupational gender segregation and its manifestations in various forms of gender bias in machine learning across English, French, and Greek. The study introduces a classification of gender biases in machine learning, providing insights into professional areas needing intervention to address gender imbalances and identifying enduring stereotypical representations in textual data. To support this, statistical analysis is conducted to explore gender variations in occupations over the past thirteen years, using official data and international classifications such as the International Standard Classification of Occupations (ISCO-08). Moreover, gendered occupational distributions are extracted from 200,920 text instances in the three languages, revealing significant discrepancies between official labour statistics and the training data.<\/jats:p>","DOI":"10.1007\/s11135-025-02261-0","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T03:30:16Z","timestamp":1751945416000},"page":"619-653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gender bias in machine learning: insights from official labour statistics and textual analysis"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5286-1388","authenticated-orcid":false,"given":"Orfeas","family":"Menis\u2013Mastromichalakis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Filandrianos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0755-1617","authenticated-orcid":false,"given":"Maria","family":"Symeonaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1378-2342","authenticated-orcid":false,"given":"Glykeria","family":"Stamatopoulou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-8636","authenticated-orcid":false,"given":"Dimitris","family":"Parsanoglou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1210-9874","authenticated-orcid":false,"given":"Giorgos","family":"Stamou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"issue":"2","key":"2261_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1111\/j.1471-6402.2004.00127.x","volume":"28","author":"MD Agars","year":"2004","unstructured":"Agars, M.D.: Reconsidering the impact of gender stereotypes on the advancement of women in organizations. Psychol. Women q. 28(2), 103\u2013111 (2004)","journal-title":"Psychol. Women q."},{"key":"2261_CR2","volume-title":"Fairness and machine learning: limitations and opportunities","author":"S Barocas","year":"2023","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and machine learning: limitations and opportunities. The MIT press, Cambridge, Massachusetts, London (2023)"},{"key":"2261_CR3","unstructured":"Binns, R.: Fairness in machine learning: lessons from political philosophy. In: Conference on fairness, accountability and transparency, PMLR, pp. 149\u2013159 (2018)"},{"issue":"9","key":"2261_CR4","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1023\/A:1023574427720","volume":"48","author":"JEO Blakemore","year":"2003","unstructured":"Blakemore, J.E.O.: Children\u2019s beliefs about violating gender norms: Boys shouldn\u2019t look like girls, and girls shouldn\u2019t act like boys. Sex Roles 48(9), 411\u2013419 (2003)","journal-title":"Sex Roles"},{"key":"2261_CR5","doi-asserted-by":"crossref","unstructured":"Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., Soricut, R., Specia, L., Tamchyna, A.: Findings of the 2014 workshop on statistical machine translation. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 12\u201358, Baltimore, Maryland, USA. Association for Computational Linguistics. https:\/\/aclanthology.org\/W14-3302\/ (2014). Accessed 4 Oct 2024","DOI":"10.3115\/v1\/W14-3302"},{"key":"2261_CR6","first-page":"4349","volume-title":"Advances in Neural Information Processing Systems","author":"T Bolukbasi","year":"2016","unstructured":"Bolukbasi, T., Chang, K.-W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 4349\u20134357. Curran Associates Inc, Barcelona (2016)"},{"issue":"2","key":"2261_CR7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/j.1540-4560.1972.tb00018.x","volume":"28","author":"IK Broverman","year":"1972","unstructured":"Broverman, I.K., Vogel, S.R., Broverman, D.M., Clarkson, F.E., Rosenkrantz, P.S.: Sex-role stereotypes: a current appraisal. J. Soc. Issues 28(2), 59\u201378 (1972)","journal-title":"J. Soc. Issues"},{"issue":"6334","key":"2261_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1126\/science.aal4230","volume":"356","author":"A Caliskan","year":"2017","unstructured":"Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356(6334), 183\u2013186 (2017). https:\/\/doi.org\/10.1126\/science.aal4230","journal-title":"Science"},{"key":"2261_CR9","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1007\/s11135-022-01405-w","volume":"57","author":"L Carlsen","year":"2023","unstructured":"Carlsen, L., Bruggemann, R., Fattore, M.: Factors determining the degree of gender equality within the European Union. Qual. Quant. 57, 1483\u20131499 (2023). https:\/\/doi.org\/10.1007\/s11135-022-01405-w","journal-title":"Qual. Quant."},{"issue":"2","key":"2261_CR10","first-page":"275","volume":"75","author":"ME Corcoran","year":"1985","unstructured":"Corcoran, M.E., Courant, P.N.: Sex role socialization and labor market outcomes. Am. Econ. Rev. 75(2), 275\u2013278 (1985)","journal-title":"Am. Econ. Rev."},{"issue":"1","key":"2261_CR11","doi-asserted-by":"publisher","first-page":"118","DOI":"10.2307\/591266","volume":"49","author":"R Crompton","year":"1998","unstructured":"Crompton, R., Harris, F.: Explaining women\u2019s employment patterns: orientations to work revisited. Br. J. Sociol.sociol. 49(1), 118\u2013136 (1998)","journal-title":"Br. J. Sociol.sociol."},{"key":"2261_CR12","doi-asserted-by":"publisher","first-page":"3087","DOI":"10.1007\/s11135-023-01790-w","volume":"58","author":"E De Gioannis","year":"2024","unstructured":"De Gioannis, E.: On the association between gender-science stereotypes\u2019 endorsement and gender bias attribution. Qual. Quant. 58, 3087\u20133106 (2024). https:\/\/doi.org\/10.1007\/s11135-023-01790-w","journal-title":"Qual. Quant."},{"key":"2261_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-024-09780-6","author":"F Ducel","year":"2024","unstructured":"Ducel, F., N\u00e9v\u00e9ol, A., Fort, K.: \u201cYou\u2019ll be a nurse, my son!\u201d automatically assessing gender biases in autoregressive language models in French and Italian. Lang. Resour. Eval.resour. Eval. (2024). https:\/\/doi.org\/10.1007\/s10579-024-09780-6","journal-title":"Lang. Resour. Eval.resour. Eval."},{"issue":"2","key":"2261_CR14","doi-asserted-by":"publisher","first-page":"210","DOI":"10.2307\/2088328","volume":"20","author":"OD Duncan","year":"1955","unstructured":"Duncan, O.D., Duncan, B.: A methodological analysis of segregation indexes. Am. Sociol. Rev. 20(2), 210\u2013217 (1955). https:\/\/doi.org\/10.2307\/2088328","journal-title":"Am. Sociol. Rev."},{"key":"2261_CR15","volume-title":"Sex differences in social behavior: a social-role interpretation","author":"AH Eagly","year":"1987","unstructured":"Eagly, A.H.: Sex differences in social behavior: a social-role interpretation. Lawrence Erlbaum Associates, Hillsdale, NJ (1987)"},{"issue":"3","key":"2261_CR16","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1037\/0033-295X.109.3.573","volume":"109","author":"AH Eagly","year":"2002","unstructured":"Eagly, A.H., Karau, S.J.: Role congruity theory of prejudice toward female leaders. Psychol. Rev. 109(3), 573\u2013598 (2002)","journal-title":"Psychol. Rev."},{"issue":"3","key":"2261_CR17","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1037\/amp0000494","volume":"75","author":"AH Eagly","year":"2020","unstructured":"Eagly, A.H., Nater, C., Miller, D.I., Kaufmann, M., Sczesny, S.: Gender stereotypes have changed: a cross-temporal meta-analysis of U.S. public opinion polls from 1946 to 2018. Am. Psychol. 75(3), 301\u2013315 (2020)","journal-title":"Am. Psychol."},{"key":"2261_CR18","unstructured":"EIGE\u2014European Institute for Gender Equality Glossary and Thesaurus: ISSN: 2467\u20133692, Catalogue number (Publications Office): MH-AC-16\u2013001-D5-I. https:\/\/eige.europa.eu\/publications-resources\/thesaurus\/overview (2024). Accessed 21 Sept 2024"},{"issue":"1","key":"2261_CR19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2307\/2579918","volume":"73","author":"P England","year":"1994","unstructured":"England, P., Herbert, M.S., Kilbourne, B.S., Reid, L.L., Megdal, L.M.: The gendered valuation of occupations and skills: earnings in 1980 census occupations. Soc. Forces 73(1), 65\u2013100 (1994)","journal-title":"Soc. Forces"},{"key":"2261_CR20","unstructured":"EU Science Hub: Gender gaps in education and employment. https:\/\/joint-research-centre.ec.europa.eu\/scientific-activities-z\/gender-gaps-education-and-employment (2024). Accessed 19 Sept 2024"},{"key":"2261_CR21","unstructured":"European Commission: Advisory Committee on Equal Opportunities for Women and Men: Opinion on \u201cBreaking gender stereotypes in the media\u201d. https:\/\/violenciagenero.igualdad.gob.es\/wp-content\/uploads\/2010_12_Opinion_Breaking_Gender_Stereotypes12.pdf. (2010). Accessed 19 Sept 2024"},{"key":"2261_CR22","unstructured":"European Commission: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee of the Regions, A Union of Equality: Gender Equality Strategy 2020\u20132025. https:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/4ed128c0-5ec5-11ea-b735-01aa75ed71a1\/language-en (2020). Accessed 10 Sept 2024"},{"key":"2261_CR23","unstructured":"European Commission: Leveraging Artificial Intelligence to maintain the ESCO Occupations Pillar, EU Directorate-General for Employment, social affairs and inclusion. https:\/\/esco.ec.europa.eu\/system\/files\/2022-03\/leveragingArtificialIntelligenceToMaintainTheESCOOccupationsPillarReport%20%281%29.pdf (2021). Accessed 19 Sept 2024"},{"key":"2261_CR24","unstructured":"EUROSTAT: European Union Labour Force Survey Database User Guide. https:\/\/www.gesis.org\/missy\/files\/documents\/EU-LFS\/eu-labour-force-survey-database-user-guide.pdf (2024). Accessed 12 Sept 2024"},{"issue":"1","key":"2261_CR25","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1177\/09589287221128438","volume":"33","author":"E Ferragina","year":"2023","unstructured":"Ferragina, E., Deeming, C.: Comparative mainstreaming? mapping the uses of the comparative method in social policy, sociology and political science since the 1970s. JESP 33(1), 132\u2013147 (2023). https:\/\/doi.org\/10.1177\/09589287221128438","journal-title":"JESP"},{"issue":"5\u20136","key":"2261_CR26","first-page":"277","volume":"77","author":"L Fox","year":"2017","unstructured":"Fox, L., Barth, J.M.: Gender-typed occupational aspirations among young children: the role of parents and teachers. Sex Roles 77(5\u20136), 277\u2013290 (2017)","journal-title":"Sex Roles"},{"issue":"2","key":"2261_CR27","first-page":"11","volume":"8","author":"J Gaskell","year":"1983","unstructured":"Gaskell, J.: Conceptions of skill and the work of women: Some historical and political issues. Atlantis-Crit. Stud. G. 8(2), 11\u201326 (1983)","journal-title":"Atlantis-Crit. Stud. G."},{"key":"2261_CR28","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Caliskan, A.: ChatGPT perpetuates gender bias in machine translation and ignores non-gendered pronouns: Findings across Bengali and five other low-resource languages. In: Proceedings of the 2023 AAAI\/ACM, Conference on AI, Ethics, and Society, pp. 901\u2013912 (2023)","DOI":"10.1145\/3600211.3604672"},{"issue":"9\u201310","key":"2261_CR29","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/BF01544212","volume":"32","author":"P Glick","year":"1995","unstructured":"Glick, P., Wilk, K., Perreault, M.: Images of occupations: components of gender and status in occupational stereotypes. Sex Roles 32(9\u201310), 565\u2013582 (1995)","journal-title":"Sex Roles"},{"key":"2261_CR30","unstructured":"Gorti, A., Gaur, M., Chadha, A. Unboxing Occupational Bias: Grounded Debiasing LLMs with US Labor Data. arXiv preprint. https:\/\/arxiv.org\/abs\/2408.11247 (2024). Accessed 15 Sept 2024"},{"issue":"6","key":"2261_CR31","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1037\/0022-0167.28.6.545","volume":"28","author":"LS Gottfredson","year":"1981","unstructured":"Gottfredson, L.S.: Circumscription and compromise: a developmental theory of occupational aspirations. J. Couns. Psychol.couns. Psychol. 28(6), 545\u2013579 (1981)","journal-title":"J. Couns. Psychol.couns. Psychol."},{"key":"2261_CR32","doi-asserted-by":"publisher","first-page":"539","DOI":"10.2307\/1130197","volume":"58","author":"WT Greenough","year":"1987","unstructured":"Greenough, W.T., Black, J.E., Wallace, C.S.: Experience and brain development. Child Dev. 58, 539\u2013559 (1987)","journal-title":"Child Dev."},{"issue":"4","key":"2261_CR33","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1111\/0022-4537.00234","volume":"57","author":"ME Heilman","year":"2001","unstructured":"Heilman, M.E.: Description and prescription: how gender stereotypes prevent women\u2019s ascent up the organizational ladder. J. Soc. Issues 57(4), 657\u2013674 (2001)","journal-title":"J. Soc. Issues"},{"key":"2261_CR34","first-page":"113","volume":"32","author":"ME Heilman","year":"2012","unstructured":"Heilman, M.E.: Gender stereotypes and workplace bias. Res. Organ. Behav. 32, 113\u2013135 (2012)","journal-title":"Res. Organ. Behav."},{"key":"2261_CR61","doi-asserted-by":"publisher","unstructured":"Heilman, M., Caleo, S., Manzi, F.: Women at Work: Pathways from Gender Stereotypes to Gender Bias and Discrimination. Annu. Rev. Organ. Psychol. Organ. Behav. 11(1), 165\u2013192 (2024). https:\/\/doi.org\/10.1146\/annurev-orgpsych-110721-034105","DOI":"10.1146\/annurev-orgpsych-110721-034105"},{"issue":"4","key":"2261_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan, A., Blomqvist, E., Cochez, M., d\u2019Amato, C., de Melo, G., Guti\u00e9rrez, C., Zimmermann, A.: Knowledge graphs. Acm Comput. Surv. 54(4), 1\u201337 (2021). https:\/\/doi.org\/10.1145\/3447772","journal-title":"Acm Comput. Surv."},{"key":"2261_CR36","unstructured":"ILO: International Standard Classification of Occupations ISCO-08, Volume I, Structure, group definitions and correspondence tables. International Labour Office, Geneva (2012)"},{"key":"2261_CR37","unstructured":"ILO: The International Standard Classification of Occupations (ISCO-08) companion guide. International Labour Office, Geneva (2023a)"},{"key":"2261_CR38","unstructured":"ILO: The International Standard Classification of Occupations (ISCO-08): Recent developments and revision. International Labour Office, Geneva (2023b)"},{"key":"2261_CR39","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1007\/s11135-016-0384-4","volume":"51","author":"HUR Khan","year":"2017","unstructured":"Khan, H.U.R., Khan, A., Zaman, K., et al.: Gender discrimination in education, health, and labour market: a voice for equality. Qual. Quant. 51, 2245\u20132266 (2017). https:\/\/doi.org\/10.1007\/s11135-016-0384-4","journal-title":"Qual. Quant."},{"key":"2261_CR40","first-page":"2611","volume":"34","author":"HR Kirk","year":"2021","unstructured":"Kirk, H.R., Jun, Y., Volpin, F., Iqbal, H., Benussi, E., Dreyer, F., Shtedritski, A., Asano, Y.: Bias out-of-the-box: an empirical analysis of intersectional occupational biases in popular generative language models. Adv. Neural. Inf. Process. Syst. 34, 2611\u20132624 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2261_CR41","doi-asserted-by":"crossref","unstructured":"Leavy, S., Meaney, G., Wade, K., Greene, D.: Mitigating gender bias in machine learning data sets. In: Bias and Social Aspects in Search and Recommendation\u2014First International Workshop BIAS 2020, Proceedings 1, Lisbon, Portugal, pp. 12\u201326. Springer International Publishing (2020)","DOI":"10.1007\/978-3-030-52485-2_2"},{"issue":"1","key":"2261_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1037\/0022-3514.66.1.37","volume":"66","author":"CN Macrae","year":"1994","unstructured":"Macrae, C.N., Milne, A.B., Bodenhausen, G.V.: Stereotypes as energy-saving devices: a peek inside the cognitive toolbox. J. Pers. Soc. Psychol. 66(1), 37\u201347 (1994)","journal-title":"J. Pers. Soc. Psychol."},{"issue":"6","key":"2261_CR43","first-page":"1","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv.comput. Surv. 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv.comput. Surv."},{"issue":"26","key":"2261_CR44","doi-asserted-by":"publisher","first-page":"10593","DOI":"10.1073\/pnas.0809921106","volume":"106","author":"BA Nosek","year":"2009","unstructured":"Nosek, B.A., Smyth, F.L., Sriram, N., Lindner, N.M., Devos, T., Ayala, A., Greenwald, A.G.: National differences in gender\u2013science stereotypes predict national sex differences in science and math achievement. Proc. Natl. Acad. Sci. U. S. a. 106(26), 10593\u201310597 (2009)","journal-title":"Proc. Natl. Acad. Sci. U. S. a."},{"issue":"2","key":"2261_CR45","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/3331166","volume":"17","author":"NF Noy","year":"2019","unstructured":"Noy, N.F., Gao, Y., Jain, A., Narayanan, A., Patterson, A., Taylor, J.: Industry-scale knowledge graphs: lessons and challenges. ACMQ 17(2), 36\u201343 (2019)","journal-title":"ACMQ"},{"key":"2261_CR46","doi-asserted-by":"publisher","unstructured":"OECD: Education at a Glance 2023: OECD Indicators. OECD Publishing, Paris. https:\/\/doi.org\/10.1787\/e13bef63-en (2023). Accessed 19 Sept 2024","DOI":"10.1787\/e13bef63-en"},{"key":"2261_CR47","unstructured":"Office for National Statistics: SOC 2020 Volume 1: structure and descriptions of unit groups. https:\/\/www.ons.gov.uk\/methodology\/classificationsandstandards\/standardoccupationalclassificationsoc\/soc2020\/soc2020volume1structureanddescriptionsofunitgroups#principles-and-concepts (2024). Accessed 10 Sept 2024"},{"key":"2261_CR48","doi-asserted-by":"publisher","unstructured":"Pan, J., Razniewski, S., Kalo, J.C., Singhania, S., Chen, J., Dietze, S., Jabeen, H., Omeliyanenko, J., Zhang, W., Lissandrini, M., Biswas, R., de Melo, G., Bonifati, A., Vakaj, E., Dragoni, M., Graux, D.: Large Language Models and Knowledge Graphs: Opportunities and Challenges. Transactions on Graph Data and Knowledge. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.2308.06374 (2023). Accessed 10 Sept 2024","DOI":"10.48550\/arXiv.2308.06374"},{"key":"2261_CR49","doi-asserted-by":"publisher","first-page":"6363","DOI":"10.1007\/s00521-019-04144-6","volume":"32","author":"MOR Prates","year":"2020","unstructured":"Prates, M.O.R., Avelar, P.H., Lamb, L.C.: Assessing gender bias in machine translation: a case study with google translate. Neural Comput. Appl.comput. Appl. 32, 6363\u20136381 (2020)","journal-title":"Neural Comput. Appl.comput. Appl."},{"issue":"140","key":"2261_CR50","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21(140), 1\u201367 (2020)","journal-title":"JMLR"},{"key":"2261_CR51","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/A:1009037101170","volume":"12","author":"C Reyna","year":"2000","unstructured":"Reyna, C.: Lazy, dumb, or industrious: when stereotypes convey attribution information in the classroom. Educ. Psychol. Rev. 12, 85\u2013110 (2000)","journal-title":"Educ. Psychol. Rev."},{"key":"2261_CR52","doi-asserted-by":"publisher","unstructured":"Salinas, A., Shah, P., Huang, Y., McCormack, R., Morstatter, F.: The unequal opportunities of large language models: examining demographic biases in job recommendations by chatgpt and llama. In: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO \u201823, pp. 1\u201315. Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3617694.3623257","DOI":"10.1145\/3617694.3623257"},{"key":"2261_CR53","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1162\/tacl_a_00401","volume":"9","author":"B Savoldi","year":"2021","unstructured":"Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., Turchi, M.: Gender bias in machine translation. Trans. Assoc. Comput. Linguist. 9, 845\u2013874 (2021)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"2261_CR54","doi-asserted-by":"crossref","unstructured":"Sheng, E., Chang, K.-W., Natarajan, P., Peng, N.: The woman worked as a babysitter: On biases in language generation. In: Proceedings of the 2019 Conference on EMNLP and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3407\u20133412. ACL, Hong Kong, China (2019). https:\/\/aclanthology.org\/D19-1339","DOI":"10.18653\/v1\/D19-1339"},{"issue":"4","key":"2261_CR55","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1108\/EDI-11-2016-0106","volume":"36","author":"M Symeonaki","year":"2017","unstructured":"Symeonaki, M., Filopoulou, C.: Quantifying gender distances in education, occupation and unemployment. Equal Divers Incl. 36(4), 340\u2013361 (2017). https:\/\/doi.org\/10.1108\/EDI-11-2016-0106","journal-title":"Equal Divers Incl."},{"key":"2261_CR56","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1002\/9781119579465.ch8","volume-title":"Data Analysis and Applications: New and Classical Approaches","author":"M Symeonaki","year":"2019","unstructured":"Symeonaki, M., Karamessini, M., Stamatopoulou, G.: Gender-based differences in the impact of the economic crisis on labour market flows in Southern Europe. In: Bozeman, J., Skiadas, C. (eds.) Data Analysis and Applications: New and Classical Approaches, pp. 107\u2013120. ISTE Science Publishing, London (2019)"},{"key":"2261_CR57","unstructured":"Thakur, V.: Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications. arXiv preprints. https:\/\/arxiv.org\/abs\/2307.09162 (2023). Accessed 15 Sept 2024."},{"key":"2261_CR58","doi-asserted-by":"publisher","unstructured":"Vanmassenhove, E.: Gender Bias in Machine Translation and the Era of Large Language Models. arXiv preprints. https:\/\/doi.org\/10.48550\/arXiv.2401.10016 (2024). Accessed 20 Sept 2024","DOI":"10.48550\/arXiv.2401.10016"},{"key":"2261_CR59","first-page":"229","volume":"5","author":"B Welle","year":"2007","unstructured":"Welle, B., Heilman, M.E.: Formal and informal discrimination against women at work. Res. Soc. Issues Manag. 5, 229\u2013252 (2007)","journal-title":"Res. Soc. Issues Manag."},{"key":"2261_CR60","doi-asserted-by":"publisher","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: Reducing gender bias amplification using corpus level constraints. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.1707.09457 (2017). Accessed 19 Sept 2024","DOI":"10.48550\/arXiv.1707.09457"}],"container-title":["Quality &amp; Quantity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11135-025-02261-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11135-025-02261-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11135-025-02261-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:45:26Z","timestamp":1771487126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11135-025-02261-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["2261"],"URL":"https:\/\/doi.org\/10.1007\/s11135-025-02261-0","relation":{},"ISSN":["1573-7845"],"issn-type":[{"value":"1573-7845","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"17 June 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}