What is the best measure of discrimination against trans people?: A systematic review of the psychometric literature
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
To understand the levels and types of discrimination experienced by a minoritised group such as trans people, it is essential that researchers have access to psychometrically sound indicators of discrimination. While a surfeit of measures exist assessing trans individuals’ experiences of transnegativity, to date, no systematic review of these instruments has been conducted. In the current study, 116 scales were evaluated on the basis of their adherence to best practices in psychometric testing. The findings indicated that, for most of the instruments assessed, limited information was provided about their psychometric properties (in particular, item development and refinement, factor structure, and scale score reliability and validity). The measures that evidenced strongest adherence to best practice recommendations in scale development are identified, and recommendations are made for the creation of new instruments assessing trans people’s experiences of transnegativity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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