A Scoping Review of Measures Assessing Gender Microaggressions Against Women
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
While considerable knowledge exists about blatant gender discrimination and violence targeting women, less is known about gender microaggressions. To understand gender microaggressions’ frequency, prevalence, and effects, researchers need robust quantitative measures. To advance gender microaggressions scholarship and support researchers’ efforts to identify high-quality measures, we conducted a psychometric scoping review. We identified 24 original, quantitative, multi-item measures designed to assess gender microaggressions or related constructs. Included measures needed at least one item assessing gender microaggressions and be used with adult women in the United States. Results indicated an increase in the number of measures including gender microaggressions’ items in recent years, with a major expansion in the number of named gender microaggressions’ measures. We found limited reporting of demographic information. Psychometric testing and characteristics varied across measures. While most ( n = 20) reported internal consistency reliability, only two-thirds ( n = 16) reported undergoing validity testing. When examining microaggressions named measures ( n = 10), we found inconsistent adherence to microaggressions’ theoretical and conceptual foundations. Substantial work remains to develop a “gold standard” measure that does not conflate subtle and blatant acts, assesses the full thematic range of gender microaggressions, and is psychometrically valid across different social contexts and diverse groups of women.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it