The Future of Women in Psychological Science
Bibliographic record
Abstract
There has been extensive discussion about gender gaps in representation and career advancement in the sciences. However, psychological science itself has yet to be the focus of discussion or systematic review, despite our field's investment in questions of equity, status, well-being, gender bias, and gender disparities. In the present article, we consider 10 topics relevant for women's career advancement in psychological science. We focus on issues that have been the subject of empirical study, discuss relevant evidence within and outside of psychological science, and draw on established psychological theory and social-science research to begin to chart a path forward. We hope that better understanding of these issues within the field will shed light on areas of existing gender gaps in the discipline and areas where positive change has happened, and spark conversation within our field about how to create lasting change to mitigate remaining gender differences in psychological science.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".