Gender inequality and self-publication patterns among scientific editors
- URL: http://arxiv.org/abs/2207.01518v1
- Date: Thu, 23 Jun 2022 10:22:01 GMT
- Title: Gender inequality and self-publication patterns among scientific editors
- Authors: Fengyuan Liu, Petter Holme, Matteo Chiesa, Bedoor AlShebli, Talal
Rahwan
- Abstract summary: We use a dataset of 103,000 editors, 240 million authors, and 220 million publications spanning five decades and 15 disciplines.
This unique dataset allows us to compare the proportion of female editors to that of female scientists in any given year or discipline.
Our dataset also allows us to study the self-publication patterns of editors, revealing that 8% of them double the rate at which they publish in their own journal soon after the editorship starts.
- Score: 3.577367523583797
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Academic publishing is the principal medium of documenting and disseminating
scientific discoveries. At the heart of its daily operations are the editorial
boards. Despite their activities and recruitment often being opaque to outside
observers, they play a crucial role in promoting fair evaluations and gender
parity. Literature on gender inequality lacks the connection between women as
editors and as research-active scientists, thereby missing the comparison
between the gender balances in these two academic roles. Literature on
editorial fairness similarly lacks longitudinal studies on the conflicts of
interest arising from editors being research active, which motivates them to
expedite the publication of their papers. We fill these gaps using a dataset of
103,000 editors, 240 million authors, and 220 million publications spanning
five decades and 15 disciplines. This unique dataset allows us to compare the
proportion of female editors to that of female scientists in any given year or
discipline. Although women are already underrepresented in science (26%), they
are even more so among editors (14%) and editors-in-chief (8%); the lack of
women with long-enough publishing careers explains the gender gap among
editors, but not editors-in-chief, suggesting that other factors may be at
play. Our dataset also allows us to study the self-publication patterns of
editors, revealing that 8% of them double the rate at which they publish in
their own journal soon after the editorship starts, and this behavior is
accentuated in journals where the editors-in-chief self-publish excessively.
Finally, men are more likely to engage in this behaviour than women.
Related papers
- Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases.
Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
arXiv Detail & Related papers (2024-06-27T19:26:11Z) - 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) - "Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in
LLM-Generated Reference Letters [97.11173801187816]
Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content.
This paper critically examines gender biases in LLM-generated reference letters.
arXiv Detail & Related papers (2023-10-13T16:12:57Z) - Characterizing the effect of retractions on scientific careers [1.6758573326215693]
Retracting academic papers is a fundamental tool of quality control when the validity of papers or the integrity of authors is questioned.
Previous studies have highlighted the adverse effects of retractions on citation counts and coauthors' citations.
Our investigation focuses on the likelihood of authors exiting scientific publishing following a retraction, and the evolution of collaboration networks.
arXiv Detail & Related papers (2023-06-11T15:52:39Z) - Gender and Prestige Bias in Coronavirus News Reporting [6.646098685534984]
We identify when experts are quoted in news and extract their names and institutional affiliations.
We find a substantial gender gap, where men are quoted three times more than women.
We also identify academic prestige bias, where journalists turn to experts from highly-ranked academic institutions more than experts from less prestigious institutions.
arXiv Detail & Related papers (2023-01-27T21:18:09Z) - How do Authors' Perceptions of their Papers Compare with Co-authors'
Perceptions and Peer-review Decisions? [87.00095008723181]
Authors have roughly a three-fold overestimate of the acceptance probability of their papers.
Female authors exhibit a marginally higher (statistically significant) miscalibration than male authors.
At least 30% of respondents of both accepted and rejected papers said that their perception of their own paper improved after the review process.
arXiv Detail & Related papers (2022-11-22T15:59:30Z) - 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) - Investigating writing style as a contributor to gender gaps in science and technology [0.0]
We find significant differences in writing style by gender, with women using more involved features in their writing.
Papers and patents with more involved features also tend to be cited more by women.
Our findings suggest that scientific text is not devoid of personal character, which could contribute to bias in evaluation.
arXiv Detail & Related papers (2022-04-28T22:33:36Z) - 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) - Gender Gap in Natural Language Processing Research: Disparities in
Authorship and Citations [31.87319293259599]
Only about 29% of first authors are female and only about 25% of last authors are female.
On average, female first authors are cited less than male first authors, even when controlling for experience and area of research.
arXiv Detail & Related papers (2020-05-03T01:31:12Z)
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.