The Construction and Validation of the Homopositivity Scale
Bibliographic record
Abstract
Social scientists appear to focus on negative beliefs about, and attitudes toward, gay men and lesbian women. This emphasis, though understandable in view of the widespread oppression of gay and lesbian individuals, is somewhat myopic because it ignores what might be referred to as the positive dimension of stereotypes. Although such a concept may appear oxymoronic, it is widely recognized that individuals may endorse a mixture of positive and negative stereotypes toward stigmatized groups such as African Americans and women. The purpose of the current series of studies (Study 1, N = 212; Study 2, N = 105) was to devise an instrument measuring endorsement of positive stereotypes about gay men (Homopositivity Scale; HPS). Two versions of the HPS (of varying length) were evaluated, with scale scores on both appearing to be internally consistent and factorially distinct from scales measuring negative stereotypes and prejudices about gay men. These studies also suggest that females are more likely than males to endorse positive stereotypes about gay men, and that such endorsement is negatively associated with need for uniqueness and need for cognition, and positively associated with media contact and benevolent sexism. The limitations of the two studies are outlined and the importance of assessing positive stereotypes about gay men in conjunction with oft-examined homonegativity is discussed.
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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 | 0.050 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".