Gender (mis)measurement: Guidelines for respecting gender diversity in psychological research
Bibliographic record
Abstract
Abstract Empirical evidence affirms that gender is a nonbinary spectrum. Yet our review of recently published empirical articles reveals that demographic gender measurement in psychology still assumes that gender comprises just two categories: women and men. This common practice is problematic. It fails to represent psychologists' current understanding of gender, violates our ethical principles as scientists, and can result in gender misclassification. Psychologists' reliance on binary measures also conveys an exclusionary attitude that is contrary to recent ethical recommendations and contrary to the growing public concern about transgender rights. We extend five simple, no‐cost recommendations that begin to resolve these ethical and methodological problems: use and report, nonbinary gender measures; report the prevalence of nonbinary participants; clarify their inclusion and treatment in analysis; and use gender inclusive language. We also address common concerns expressed by researchers, including whether measuring “sex” resolves the issue and whether gender‐inclusive measures confuse or offend participants.
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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 | 0.673 | 0.770 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.015 | 0.021 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".