Gender Representation in Brazilian Computer Science Conferences

• URL: http://arxiv.org/abs/2208.11020v1
• Date: Tue, 23 Aug 2022 15:10:10 GMT
• Title: Gender Representation in Brazilian Computer Science Conferences
• Authors: Nat\'alia Dal Pizzol, Eduardo Dos Santos Barbosa, Soraia Raupp Musse
• Abstract summary: 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.
• Score: 0.6961253535504979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents an automated bibliometric analysis of 6569 research papers published in thirteen Brazilian Computer Science Society (SBC) conferences from 1999 to 2021. Our primary goal was to gather data to understand the gender representation in publications in the field of Computer Science. We applied a systematic assignment of gender to 23.573 listed papers authorships, finding that the gender gap for women is significant, with female authors being under-represented in all years of the study.

Related papers

• Women's Participation in Computing: Evolving Research Methods [0.0]
  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.
  arXiv  Detail & Related papers  (2024-07-25T00:05:18Z)
• 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)
• The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages [51.2321117760104]
  This paper describes the Gender-GAP Pipeline, an automatic pipeline to characterize gender representation in large-scale datasets for 55 languages. The pipeline uses a multilingual lexicon of gendered person-nouns to quantify the gender representation in text. We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation.
  arXiv  Detail & Related papers  (2023-08-31T17:20:50Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• 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)
• Temporal Analysis and Gender Bias in Computing [0.0]
  Many names change ascribed gender over decades: the "Leslie problem" This article identifies 300 given names with measurable "gender shifts" across 1925-1975. This article demonstrates, quantitatively, there is net "female shift" that likely results in the overcounting of women (and undercounting of men) in earlier decades.
  arXiv  Detail & Related papers  (2022-09-29T00:29:43Z)
• 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)
• The effect of the COVID-19 pandemic on gendered research productivity and its correlates [0.0]
  This study examined how the proportion of female authors in academic journals on a global scale changed in 2020. We observed a decrease in research productivity for female researchers in 2020, mostly as first authors, followed by last author position. Female researchers were not necessarily excluded from but were marginalised in research.
  arXiv  Detail & Related papers  (2021-11-29T06:20:44Z)
• 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)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

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.