Examining personality psychology to unpack the peer review system: Towards a more diverse, inclusive, and equitable psychological science
Bibliographic record
Abstract
Peer review serves to evaluate the scientific validity and quality of research by other researchers within the same field. Psychology, like many other disciplines, uses peer review to determine whether researchers’ work is published, where it is published, and in what form. However, the peer review system is imperfect, often perpetuating harm, exclusion, and inequities. We believe these problems hinder psychology from becoming a truly representative and valid science. In this paper, we uncover the historical roots of the peer review system in psychology and describe how these roots persist today. Then, using personality psychology as an exemplar, we leverage a social justice lens to: (1) document key contemporary problems in the peer review system; (2) identify global challenges in peer review; and (3) provide recommendations and guiding questions that might help the field become more equitable, diverse, just, and inclusive. To do so, we draw upon three sources of information: our personal experiences, qualitative and quantitative data from 104 self-identifying personality psychologists, and the prior literature. Ultimately, the goal of this work is to give voice to those who have been harmed in peer review, and to inform how we might reimagine peer review in personality psychology and beyond.
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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 | 0.171 | 0.297 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".