A Systematic Review of the Psychometric Properties of Binegativity scales
Bibliographic record
Abstract
Research suggests that bisexual people encounter discrimination from heterosexual people, lesbian women, and gay men. This discrimination, henceforth labeled ‘binegativity,’ manifests itself in various forms such as denying and delegitimizing bisexuality as a sexual orientation, rendering bisexual persons invisible from the larger queer community, and promulgating various myths about bisexual persons (e.g., the myth that bisexual persons are intrinsically more promiscuous than their nonbisexual counterparts). Binegativity has the potential to compromise bisexual individuals’ mental and physical health and, consequently, their experiences of discrimination are being assessed using a wide variety of measures. To date, a review of the psychometric properties of the scales utilized to measure binegative experiences has not been published. To address this omission, a systematic review was completed to determine each scale’s adherence to best practices in psychometric development and testing. Forty-one studies using 30 unique scales were awarded a score from 0 to 5 (i.e., higher scores denote greater psychometric soundness). The Anti-Bisexual Experiences Scale emerged as the strongest measure of experienced binegativity. Other measures are identified and reviewed, and recommendations for future research are offered.
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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 | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".