Racism and censorship in the editorial and peer review process
Bibliographic record
Abstract
Psychology aims to capture the diversity of our human experience, yet racial inequity ensures only specific experiences are studied, peer-reviewed, and eventually published. Despite recent publications on racial bias in research topics, study samples, academic teams, and publication trends, bias in the peer review process remains largely unexamined. Drawing on compelling case study examples from APA and other leading international journals, this article proposes key mechanisms underlying racial bias and censorship in the editorial and peer review process, including bias in reviewer selection, devaluing racialized expertise, censorship of critical perspectives, minimal consideration of harm to racialized people, and the publication of unscientific and racist studies. The field of psychology needs more diverse researchers, perspectives, and topics to reach its full potential and meet the mental health needs of communities of colour. Several recommendations are called for to ensure the APA can centre racial equity throughout the editorial and review process.
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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 | 0.172 | 0.434 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".