The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code
- URL: http://arxiv.org/abs/2410.01454v1
- Date: Wed, 2 Oct 2024 12:03:37 GMT
- Title: The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code
- Authors: Annalí Casanueva, Davide Rossi, Stefano Zacchiroli, Théo Zimmermann,
- Abstract summary: COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code.
Specifically, COVID-19 affected women hobbyists, identified using contribution patterns and email address domains.
- Score: 6.413512495984789
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite its promise of openness and inclusiveness, the development of free and open source software (FOSS) remains significantly unbalanced in terms of gender representation among contributors. To assist open source project maintainers and communities in addressing this imbalance, it is crucial to understand the causes of this inequality.In this study, we aim to establish how the COVID-19 pandemic has influenced the ability of women to contribute to public code. To do so, we use the Software Heritage archive, which holds the largest dataset of commits to public code, and the difference in differences (DID) methodology from econometrics that enables the derivation of causality from historical data.Our findings show that the COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code, relatively to men. Further, our observations of specific contributor subgroups indicate that COVID-19 particularly affected women hobbyists, identified using contribution patterns and email address domains.
Related papers
- Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index [0.0]
Community Sentiment and Engagement Index (CSEI) developed to capture nuanced public sentiment and engagement variations on social media.
CSEI's responsiveness was validated using a dataset of 4,510,178 posts about COVID-19.
arXiv Detail & Related papers (2024-12-22T08:52:12Z) - Gender Disparities in Contributions, Leadership, and Collaboration: An Exploratory Study on Software Systems Research [1.8049331600471712]
We analyzed 2,000 articles published over the past decade in the Journal of Systems and Software.
Our analysis showed that only 32.74% of the total authors are women and female-led or supervised studies were fewer than those of men.
Third, we explored the areas of software systems research and found that female authors are more actively involved in human-centric research domains.
arXiv Detail & Related papers (2024-12-20T08:20:23Z) - Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
We study gender bias in 22 popular image-to-text vision-language assistants (VLAs)
Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances.
To eliminate the gender bias in these models, we find that finetuning-based debiasing methods achieve the best tradeoff between debiasing and retaining performance on downstream tasks.
arXiv Detail & Related papers (2024-10-25T05:59:44Z) - GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases.
GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
arXiv Detail & Related papers (2024-08-22T15:35:46Z) - Assessing the Influence of Toxic and Gender Discriminatory Communication on Perceptible Diversity in OSS Projects [2.526146573337397]
The presence of toxic and gender-identity derogatory language in open-source software (OSS) communities has recently become a focal point for researchers.
This study aims to investigate how such content influences the gender, ethnicity, and tenure diversity of open-source software development teams.
arXiv Detail & Related papers (2024-03-12T22:48:21Z) - Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable.
Existing susceptibility studies heavily rely on self-reported beliefs.
We propose a computational approach to model users' latent susceptibility levels.
arXiv Detail & Related papers (2023-11-16T07:22:56Z) - Investigating Participation Mechanisms in EU Code Week [68.8204255655161]
Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and use of digital technologies.
The aim of the manuscript is to offer a detailed and comprehensive statistical description of Code Week's participation in the EU Member States.
arXiv Detail & Related papers (2022-05-29T19:16:03Z) - Measuring Fairness Under Unawareness of Sensitive Attributes: A
Quantification-Based Approach [131.20444904674494]
We tackle the problem of measuring group fairness under unawareness of sensitive attributes.
We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
arXiv Detail & Related papers (2021-09-17T13:45:46Z) - Gender Data 4 Girls?: A Postcolonial Feminist Participatory Study in
Bangladesh [0.0]
Postcolonial feminism remains underutilised for critically investigating data for development projects.
This paper presents the findings from a participatory action research project with young women involved in a gender data for development project in Bangladesh.
arXiv Detail & Related papers (2021-08-23T11:41:27Z) - Questioning causality on sex, gender and COVID-19, and identifying bias
in large-scale data-driven analyses: the Bias Priority Recommendations and
Bias Catalog for Pandemics [0.0]
We highlight the challenge of making causal claims based on available data, given the lack of statistical significance and potential existence of biases.
We have compiled an encyclopedia-like reference guide, the Bias Catalog for Pandemics, to provide definitions and emphasize realistic examples of bias in general.
The objective is to anticipate and avoid disparate impact and discrimination, by considering causality, explainability, bias and techniques to the latter.
arXiv Detail & Related papers (2021-04-29T17:07:06Z) - Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by
Ranking Algorithms [68.85295025020942]
We propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a Search Engines to support gender stereotypes.
GSR is the first specifically tailored measure for Information Retrieval, capable of quantifying representational harms.
arXiv Detail & Related papers (2020-09-02T20:45:04Z)
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.