A Psychometric Review of Measures Assessing Discrimination Against Sexual Minorities
Bibliographic record
Abstract
Discrimination against sexual minorities is widespread and has deleterious consequences on victims' psychological and physical wellbeing. However, a review of the psychometric properties of instruments measuring lesbian, gay, and bisexual (LGB) discrimination has not been conducted. The results of this review, which involved evaluating 162 articles, reveal that most have suboptimal psychometric properties. Specifically, myriad scales possess questionable content validity as (1) items are not created in collaboration with sexual minorities; (2) measures possess a small number of items and, thus, may not sufficiently represent the domain of interest; and (3) scales are "adapted" from measures designed to examine race- and gender-based discrimination. Additional limitations include (1) summed scores are computed, often in the absence of scale score reliability metrics; (2) summed scores operate from the questionable assumption that diverse forms of discrimination are necessarily interrelated; (3) the dimensionality of instruments presumed to consist of subscales is seldom tested; (4) tests of criterion-related validity are routinely omitted; and (5) formal tests of measures' construct validity are seldom provided, necessitating that one infer validity based on the results obtained. The absence of "gold standard" measures, the attendant difficulty in formulating a coherent picture of this body of research, and suggestions for psychometric improvements are noted.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".