An official website of the United States government
Many notable female sociologists have vanished from the canonical history of American sociology. As the most influential crowd-sourced encyclopedia, Wikipedia promises – but does not necessarily deliver – a democratic corrective to the generation of knowledge, including academic knowledge. This article explores multiple mechanisms by which women either enter or disappear from the disciplinary record by analyzing the unfolding interaction between the canonical disciplinary history of sociology and Wikipedia. We argue that the uneven representation of women sociologists as (1) remembered, (2) neglected, (3) erased or, finally, (4) recovered is shaped by the emerging interactional space of knowledge production.
more »
« less
Who Counts as a Notable Sociologist on Wikipedia? Gender, Race and the ‘Professor Test’
This paper documents and estimates the extent of underrepresentation of women and people of color on the pages of Wikipedia devoted to contemporary American sociologists. In contrast to the demographic diversity of the discipline, sociologists represented on Wikipedia are largely white men. The gender and racial/ethnic gaps in likelihood of representation have exhibited little change over time. Using novel data, we estimate the “risk” of having a Wikipedia page for a sample of contemporary sociologists. We show that the observed differences (in academic rank, length of career, and notability measured with both H-index and departmental reputation) between men and women sociologists and whites and nonwhites, respectively, explain only about half of the differences in the likelihood of being represented on Wikipedia. The article also enumerates both supply- and demand-side mechanisms that may account for these continuing gaps in representation.
more »
« less
- Award ID(s):
- 1322971
- PAR ID:
- 10086412
- Date Published:
- Journal Name:
- Socius
- Volume:
- 5
- ISSN:
- 2378-0231
- Page Range / eLocation ID:
- 1-14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Millions of people worldwide contribute content to peer production repositories that serve human information needs and provide vital world knowledge to prominent artificial intelligence systems. Yet, extreme gender participation disparities exist in which men significantly outnumber women. A central concern has been that due to self-focus bias, these disparities can lead to corresponding gender content disparities, in which content of interest to men is better represented than content of interest to women. This paper investigates the relationship between participation and content disparities in OpenStreetMap. We replicate findings that women are dramatically under-represented as OSM contributors, and observe that men and women contribute different types of content and do so about different places. However, the character of these differences confound simple narratives about self-focus bias: we find that on a proportional basis, men produced a higher proportion of contributions in feminized spaces compared to women, while women produced a higher proportion of contributions in masculinized spaces compared to men.more » « less
-
To recognize and mitigate the harms of generative AI systems, it is crucial to consider who is represented in the outputs of generative AI systems and how people are represented. A critical gap emerges when naively improving who is represented, as this does not imply bias mitigation efforts have been applied to address how people are represented. We critically examined this by investigating gender representation in occupation across state-of-the-art large language models. We first show evidence suggesting that over time there have been interventions to models altering the resulting gender distribution, and we find that women are more represented than men when models are prompted to generate biographies or personas. We then demonstrate that representational biases persist in how different genders are represented by examining statistically significant word differences across genders. This results in a proliferation of representational harms, stereotypes, and neoliberalism ideals that, despite existing interventions to increase female representation, reinforce existing systems of oppression.more » « less
-
Abstract This paper estimates the long-run impacts of banning affirmative action on men and women from under-represented minority (URM) racial and ethnic groups in the United States. Using data from the US Census and American Community Survey, we use a difference-in-differences framework to compare the college degree completion, graduate degree completion, earnings, and employment of URM individuals to non-URM individuals before and after affirmative action bans went into effect across several US states. We also employ event study analyses and alternative estimators to confirm the validity of our approach and discuss the generalizability of the findings. Results suggest that banning affirmative action results in a decline in URM women’s college degree completion, earnings, and employment relative to non-Hispanic White women, driven largely by impacts on Hispanic women. Thus, affirmative action bans resulted in an increase in racial/ethnic disparities in both college degree completion and earnings among women. Effects on URM men are more ambiguous and indicate significant heterogeneity across states, with some estimates pointing to a possible positive impact on labour market outcomes of Black men. These results suggest that the relative magnitude of college quality versus mismatch effects vary for URM men and women and highlight the importance of disaggregating results by gender, race, and ethnicity. We conclude by discussing how our results compare with others in the literature and directions for future research.more » « less
-
Textbooks shape teaching and learning in introductory biology and highlight scientists as potential role models who are responsible for significant discoveries. We explore a potential demographic mismatch between the scientists featured in textbooks and the students who use textbooks to learn core concepts in biology. We conducted a demographic analysis by extracting hundreds of human names from common biology textbooks and assessing the binary gender and race of featured scientists. We found that the most common scientists featured in textbooks are white men. However, women and scientists of colour are increasingly represented in contemporary scientific discoveries. In fact, the proportion of women highlighted in textbooks has increased in lockstep with the proportion of women in the field, indicating that textbooks are matching a changing demographic landscape. Despite these gains, the scientists portrayed in textbooks are not representative of their target audience—the student population. Overall, very few scientists of colour were highlighted, and projections suggest it could take multiple centuries at current rates before we reach inclusive representation. We call upon textbook publishers to expand upon the scientists they highlight to reflect the diverse population of learners in biology.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1177/2378023118823946
