Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.137 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it