Bibliographic record
Abstract
Women are notably underrepresented in the academic sciences. Psychology is a pertinent case study of gender inequality in science, because women make up over three quarters of undergraduate and graduate students but only a third of all full professors. Here, publication records from 125 high-impact, peer-reviewed psychology journals are analyzed to describe nuanced patterns about how men and women contribute to research psychology. To determine gender, we classified over 750,000 authors on 200,000 unique publications by comparing the 1st name of each author to openly available census data. The data replicate previous reports of publication and citation gender gaps in psychology and significantly extend these results by showing that these gaps are persistent across subdiscipline and time but are mediated by various contextual factors. For example, although the size of the publication and citation gaps are not explained by the university affiliation of the authors' and frequency of coauthorship, the gaps are larger in high-impact journals and at the last-author position. These patterns have remained largely unchanged since at least 2003. These results provide a detailed look at the variety of factors contributing to the differences in how men and women publish in research psychology and provide free and openly available tools for assessing publication and citation differences across time, journals, and other academic disciplines. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".