Scientists & Women Scientists: Exploring Gender Biases in Institutional Category Systems
Bibliographic record
Abstract
For many categories of people, men are perceived as the more default or typical members whereas women are perceived as more atypical. This bias can lead to an asymmetry in the existence and frequency of categories marked by gendered language. Here we explore the extent to which this asymmetry exists in two institutional category systems: the Library of Congress Subject Headings (LCSH) and English Wikipedia. We find that the LCSH exhibits more bias towards women than Wikipedia, and that in the LCSH this bias has not changed in the last 30 years, whereas Wikipedia shows a noticeable increase in gender balanced categories during the early 2010s. These findings suggest that more can be done to reduce gender bias in the LCSH and demonstrate how principles of typicality and categorization play out in real-world settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.024 | 0.033 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".