A Systematic Review of the Psychometric Properties of Composite LGBT Prejudice and Discrimination Scales
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
Prejudice and discrimination against LGBT individuals is widespread and has been shown to have negative consequences for sexual and gender minority persons' physical and psychological wellbeing. A recent and problematic trend in the literature is to compositely measure prejudice toward and discrimination against LGBT persons. As such, a review of the psychometric properties of scales assessing, in a combinatory fashion, negative attitudes and/or behaviors toward LGBT persons is warranted. In the current study, 32 scales were identified, and their psychometric properties were evaluated. Most of the scales reviewed did not provide sufficient information regarding item development and refinement, scale dimensionality, scale score reliability, or validity. Properties of the reviewed scales are summarized, and recommendations for better measurement practice are articulated.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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