How well are outgroup attitudes and behaviours toward bisexual individuals measured? A systematic review of the psychometric properties of binegativity measures
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
Bisexual individuals not only experience instances of invisibility and erasure within various social systems and structures but also encounter prejudice and discrimination that is fuelled by stereotypes. While gay men and lesbian women tend to only experience homonegativity from heterosexual individuals, bisexual persons are subject to binegativity from both heterosexual and gay/lesbian groups. Only recently has there been a concerted effort to increase the amount of research conducted on binegativity among heterosexual and gay/lesbian groups. However, since this body of research is only recently growing, it seems prudent to identify the binegativity measures currently available and evaluate how well each adheres to best practices regarding their psychometric properties. In the current study, 82 scales were identified and their adherence to best practices in psychometric assessment was examined. Most measures lacked sufficient details attesting to item development/refinement, factor structure, scale score reliability, and construct validity. No measure was found to follow best practices for all psychometric properties; those that were close are identified and recommendations are made for improving future binegativity scales.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.001 | 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