References
- Ahuja, M. K. (2002). Women in the information technology profession: A literature review, synthesis and research agenda. European Journal of Information Systems, 11(1), 20–34. https://doi.org/https://doi.org/10.1057/palgrave.ejis.3000417
- ALA Organization. (n.d.). Information technology and libraries–about the journal. https://ejournals.bc.edu/index.php/ital/about.
- Anderson, N., Timms, C., & Courtney, L. (2006). If you want to advance in the ICT industry, you have to work harder than your male peers. In Women in ICT Industry Survey: Preliminary finding. Proceedings of AusWIT: Participation one year on, 10th Australian Women in IT conference. Adelaide, Australia.
- Ball, P. (2003). Computer program detects author gender. Nature. https://doi.org/https://doi.org/10.1038/news030714-13
- Banaji, M. R., & Greenwald, A. G. (2016). Blindspot: Hidden biases of good people. Bantam.
- Bierema, L. L., & Merriam, S. B. (2002). E-mentoring: Using computer mediated communication to enhance the mentoring process. Innovative Higher Education, 26(3), 211–227. https://doi.org/https://doi.org/10.1023/A:1017921023103
- Blackburn, H. (2015). Factors that influence male millennials to become professional librarians. [Doctoral dissertation]
- Bolukbasi, T., Chang, K., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Advances in Neural Information Processing Systems, 29, 4349–4357.
- Braun, V., Clarke, V., & Cooper, H. (2012). APA handbook of research methods in psychology. Cooper H, Thematic Analysis, 2, 77–101.
- Code4Lib. (n.d.). Code4Lib mission. https://journal.code4lib.org/mission.
- Cordell, R. (2020). Machine learning + libraries: A report on the state of the field. https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf
- Dean, G. (2015). The shock of the familiar: Three timelines about gender and technology in the library. Digital Humanities Quarterly, 9(2)
- do Rosário, C. R., Amaral, F. G., Kuffel, F. J. M., Kipper, L. M., & Frozza, R. (2021). Using Bayesian belief networks to improve distributed situation awareness in shift changeovers: A case study. Expert Systems with Applications, 188.
- Exline, E. (2014). Gender composition and salary gaps in Association of Research Libraries (ARL) institutions. GenderWatch; ProQuest Dissertations & Theses Global. https://rider.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/gender-composition-salary-gaps-association/docview/1654999554/se-2?accountid=37385
- Feldman, T., & Peake, A. (2021). On the basis of sex: A review of gender bias in machine learning applications. arXiv Preprint arXiv:2104.02532
- Fisher, W. E. (2005). Abstract writing. The Journal of Surgical Research, 128(2), 162–164. https://doi.org/https://doi.org/10.1016/j.jss.2005.07.007.
- Foster, B. (2020). Information literacy beyond librarians: A data/methods triangulation approach to investigating disciplinary IL teaching practices. Evidence Based Library and Information Practice, 15(1), 20–37. https://doi.org/https://doi.org/10.18438/eblip29635
- Frankel, F., & Reid, R. (2008). Big data: Distilling meaning from data. Nature, 455(7209), 30–30. https://doi.org/https://doi.org/10.1038/455030a
- Gokhale, A. A., & Stier, K. (2004). Closing the gender gap in technical disciplines: An investigative study. Journal of Women and Minorities in Science and Engineering, 10(2), 149–160. https://doi.org/https://doi.org/10.1615/JWomenMinorScienEng.v10.i2.30
- Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias: From discrimination discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 2125–2126).
- Hendrigan, H. (2019). Mixing digital humanities and applied science librarianship: Using Voyant tools to reveal word patterns in faculty research. Issues in Science and Technology Librarianship, (91). https://doi.org/https://doi.org/10.29173/istl3
- Hetenyi, G., Lengyel, A. D., & Szilasi, M. D. (2019). Quantitative analysis of qualitative data: Using voyant tools to investigate the sales-marketing interface. Journal of Industrial Engineering and Management, 12(3), 393–404. https://doi.org/https://doi.org/10.3926/jiem.2929
- Hildebrand, S. (1999). The information age vs. gender equity. Library Journal, 124(7), 44–47.
- Kenny, E. J., & Donnelly, R. (2020). Navigating the gender structure in information technology: How does this affect the experiences and behaviours of women? Human Relations, 73(3), 326–350. https://doi.org/https://doi.org/10.1177/0018726719828449
- Kiselev, A. (2021). Using voyant tools with Japanese Youtube comments. The European Journal of Humanities and Social Sciences, (4), 38–43. https://doi.org/https://doi.org/10.29013/EJHSS-21-4-38-43
- Kracker, J., & Pollio, H. R. (2003). The experience of libraries across time: Thematic analysis of undergraduate recollections of library experiences. Journal of the American Society for Information Science and Technology, 54(12), 1104–1116. https://doi.org/https://doi.org/10.1002/asi.10309.
- Lamont, M. (2009). Gender, technology, and libraries. Information Technology and Libraries, 28(3), 137–142. https://doi.org/https://doi.org/10.6017/ital.v28i3.3221
- Leavy, S. (2018, 14–16 May/June). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning [Paper presentation]. Proceedings of the 40th International Conference on Software Engineering, Gothenburg, Sweden. https://dl.acm.org/citation.cfm?id=3195580
- Leavy, S., Meaney, G., Wade, K., & Greene, D. (2020). Mitigating gender bias in machine learning data sets. In: L. Boratto, S. Faralli, M. Marras, & G. Stilo (Eds.), Bias and social aspects in search and recommendation. BIAS 2020. Communications in Computer and Information Science, Vol. 1245. Springer. https://doi.org/https://doi.org/10.1007/978-3-030-52485-2_2
- Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In: C. Nédellec, C. Rouveirol (Eds.), Machine learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, Vol. 1398. Springer. https://doi.org/https://doi.org/10.1007/BFb0026666
- Library hi tech journal description. (n.d.). https://www.emeraldgrouppublishing.com/journal/lht?ga=2.109411397.1378987102.1635777058-303736353.1635777058
- Mathews, J. M., & Pardue, H. (2009). The presence of IT skill sets in librarian position announcements. College & Research Libraries, 70(3), 250–257. https://doi.org/https://doi.org/10.5860/0700250
- Miller, A. (2018). Text mining digital humanities projects: Assessing content analysis capabilities of Voyant tools. Journal of Web Librarianship, 12(3), 169–197. https://doi.org/https://doi.org/10.1080/19322909.2018.1479673
- Neigel, C. (2015). LIS leadership and leadership education: A matter of gender. Journal of Library Administration, 55(7), 521–534. https://doi.org/https://doi.org/10.1080/01930826.2015.1076307
- Padilla, T. (2019). Responsible operations: Data science, machine learning, and AI in libraries (OCLC Research Position Paper). OCLC Online Computer Library Center, Inc.
- Reddy, T. R., Vardhan, B. V., GopiChand, M., & Karunakar, K. (2018). Gender prediction in author profiling using ReliefF feature selection algorithm. In V. Bhateja, C. Coello Coello, S. Satapathy, & P. Pattnaik (Eds.), Intelligent engineering informatics. Advances in Intelligent Systems and Computing. Vol. 695 Springer. https://doi.org/https://doi.org/10.1007/978-981-10-7566-7_18
- Rice, P. L., & Ezzy, D. (1999). Qualitative research methods: A health focus. Oxford University Press.
- Ricigliano, L., & Houston, R. (2003). Men’s work, women’s work: The social shaping of technology in academic libraries [Paper presentation]. Association of College and Research Libraries 11th Annual National Conference, Charlotte, NC, 1.
- Rosser, S. V. (2006). Using the lenses of feminist theories to focus on women and technology. In M. F. Fox, D. G. Johnson, & S. V. Rosser (Eds.), Women, gender, and technology (pp. 13–46). University of Illinois Press.
- Saldaña, J. (2021). The coding manual for qualitative researchers. Sage Publications.
- Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Springer.
- Sumner, M., & Niederman, F. (2004). The impact of gender differences on job satisfaction, job turnover, and career experiences of information systems professionals. Journal of Computer Information Systems, 44(2), 29–39.
- Terry, J. L. (1996). Authorship in college & research libraries revisited: Gender, institutional affiliation, collaboration. College & Research Libraries, 57(4) 377–383. https://doi.org/https://doi.org/10.5860/crl_57_04_377
- Tokarz, R. E. (2017). Identifying criminal justice faculty research interests using Voyant and NVivo. Behavioral & Social Sciences Librarian, 36(3), 113–121. https://doi.org/https://doi.org/10.1080/01639269.2017.1771034
- Trauth, E. M. (2006). Theorizing gender and information technology research. In Encyclopedia of gender and information technology (pp. 1154–1159). IGI Global.
- Trauth, E. M., Quesenberry, J. L., & Huang, H. (2009). Retaining women in the US IT workforce: theorizing the influence of organizational factors. European Journal of Information Systems, 18(5), 476–497. https://doi.org/https://doi.org/10.1057/ejis.2009.31
- U.S. Department of Labor. (2019). Current population survey, annual averages, Table 11. Bureau of Labor Statistics.
- Verma, M. (2011). Barriers to career advancement of women in Indian it industry–a conceptual framework. Metamorphosis, 10(1), 29–41. https://doi.org/https://doi.org/10.1177/0972622520110105
- Vogt, S. (2003). The best man for the job is a woman? Information Today, 20(3), 23.
- Wajcman, J. (2000). Reflections on gender and technology studies: in what state is the art? Social Studies of Science, 30(3), 447–464. https://doi.org/https://doi.org/10.1177/030631200030003005
- Wajcman, J. (2006). The feminization of work in the information age. In M. Fox, D. Johnson, & S. Rosser (Eds.), Women, gender, and technology (pp. 80–97). University of Illinois Press.
- Wasburn, M. H., & Miller, S. G. (2006). Still a chilly climate for women students in technology: A case study. In V. S. Rosser & M. F. Fox (Eds.), Women, Gender, and Technology (pp. 60–79). University of Illinois Press.
- Whitfield, S., & Johnson, A. T. (2019). Women technology librarians as good citizens. The Journal of Academic Librarianship, 45(5), 102058. https://doi.org/https://doi.org/10.1016/j.acalib.2019.102058
- Wiebe, T. J. (2004). Issues faced by male librarians: Stereotypes, perceptions, and career ramifications. Colorado Libraries, 31(1), 11–13.
- Wilbur, W. J., & Kim, W. (2009). The ineffectiveness of within - document term frequency in text classification. Information Retrieval, 12(5), 509–525. https://doi.org/https://doi.org/10.1007/s10791-008-9069-5.
- Wilson, F. (2003). Can compute, won’t compute: Women's participation in the culture of computing. New Technology, Work and Employment, 18(2), 127–142. https://doi.org/https://doi.org/10.1111/1468-005X.00115
- Wilson, M. (2004). A conceptual framework for studying gender in information systems research. Journal of Information Technology, 19(1), 81–92. https://doi.org/https://doi.org/10.1057/palgrave.jit.2000008
- Zahedzadeh, G. (2017). Overt attacks and covert thoughts. Aggression and Violent Behavior, 36, 1–8. https://doi.org/https://doi.org/10.1016/j.avb.2017.06.009