Women's Participation in Computing: Evolving Research Methods
- URL: http://arxiv.org/abs/2407.17677v1
- Date: Thu, 25 Jul 2024 00:05:18 GMT
- Title: Women's Participation in Computing: Evolving Research Methods
- Authors: Thomas J. Misa,
- Abstract summary: A 2022 keynote for the ACM History Committee on "Why SIG History Matters: New Data on Gender Bias in ACM's Founding SIGs 1970-2000"
presented new data describing women's participation as research-article authors in 13 early ACM Special Interest Groups.
This report expands on these earlier articles, and their evolving research method, connecting them to the ACM SIG Heritage presentation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A 2022 keynote for the ACM History Committee on "Why SIG History Matters: New Data on Gender Bias in ACM's Founding SIGs 1970-2000" presented new data describing women's participation as research-article authors in 13 early ACM Special Interest Groups, finding significant growth in women's participation across 1970-2000 and, additionally, remarkable differences in women's participation between the SIGs. That presentation built on several earlier publications that developed a research method for assessing the number of women computer scientists that [a] are chronologically prior to the availability of the Bureau of Labor Statistics (BLS) data on women in the IT workforce; and [b] permit focused investigation of varied sub-fields within computing. This present report expands on these earlier articles, and their evolving research method, connecting them to the ACM SIG Heritage presentation. It also outlines some of the choices and considerations made in developing and refining "mixed methods" research (using both quantitative and qualitative approaches) as well as extensions of the research being currently explored.
Related papers
- Report on Female Participation in Informatics degrees in Europe [3.498239025413087]
This study aims to enrich and leverage data from the Informatics Europe Higher Education (IEHE) data portal.
The research examines the proportion of female students, first-year enrollments, and degrees awarded to women in the field.
arXiv Detail & Related papers (2024-10-15T09:33:16Z) - Dynamics of Gender Bias within Computer Science [0.0]
ACM SIGs expanded during 1970-2000; each experienced increasing women's authorship.
Several SIGs had fewer than 10% women authors while SIGUCCS exceeded 40%.
Three SIGs experienced accelerating growth in women's authorship; most, including a composite ACM, had decelerating growth.
arXiv Detail & Related papers (2024-07-11T00:14:21Z) - Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
We investigate the role of social media influencers in enhancing the visibility of machine learning research.
We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023.
Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
arXiv Detail & Related papers (2024-01-24T20:05:49Z) - De-identification of clinical free text using natural language
processing: A systematic review of current approaches [48.343430343213896]
Natural language processing has repeatedly demonstrated its feasibility in automating the de-identification process.
Our study aims to provide systematic evidence on how the de-identification of clinical free text has evolved in the last thirteen years.
arXiv Detail & Related papers (2023-11-28T13:20:41Z) - Gender Bias in Computing [0.0]
It offers new quantitative data on the computing workforce prior to the availability of US Census data in the 1970s.
A novel method of gender analysis is developed to estimate women's and men's participation in computing beginning in the 1950s.
arXiv Detail & Related papers (2022-10-29T00:10:25Z) - Gender Representation in Brazilian Computer Science Conferences [0.6961253535504979]
This study presents an automated bibliometric analysis of 6569 research papers published in thirteen Brazilian Computer Science Society (SBC) conferences from 1999 to 2021.
We applied a systematic assignment of gender to 23.573 listed papers authorships, finding that the gender gap for women is significant.
arXiv Detail & Related papers (2022-08-23T15:10:10Z) - Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature.
We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
arXiv Detail & Related papers (2022-07-18T11:38:32Z) - Fairness in Recommender Systems: Research Landscape and Future
Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past.
We present an overview of how research in this field is currently operationalized.
Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z) - Towards Understanding Gender-Seniority Compound Bias in Natural Language
Generation [64.65911758042914]
We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models.
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
arXiv Detail & Related papers (2022-05-19T20:05:02Z) - Assessing Gender Bias in the Information Systems Field: An Analysis of
the Impact on Citations [0.0]
This paper outlines a study to estimate the impact of scholarly citations that female IS academics accumulate vis-a-vis their male colleagues.
By doing so we propose to contribute knowledge on a core dimension of gender bias in academia, which is, so far, almost completely unexplored in the IS field.
arXiv Detail & Related papers (2021-08-22T18:18:52Z) - Gender bias in magazines oriented to men and women: a computational
approach [58.720142291102135]
We compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group over a decade.
With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time.
Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time.
arXiv Detail & Related papers (2020-11-24T14:02:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.