Individual difference predictors of the Attitudes Towards Asexuality scale
Bibliographic record
Abstract
While predictors of attitudes toward lesbians and gay men, as well as bisexuals and trans individuals, have been investigated relatively thoroughly, attitudes toward asexuality are a recently emerging field. The current study investigates predictors of attitudes toward asexuality, operationally defined using the Attitudes Towards Asexuality (ATA) scale created by Hoffarth and colleagues in 2016 . Predictors included authoritarianism, social dominance orientation, intergroup disgust sensitivity, sexism, erotophobia–erotophilia, sociosexuality, motivation to respond without prejudice, singlism, and demographic characteristics of the perceiver. Response to the ATA indicated positivity toward asexuality, with the majority of participants expressing disagreement with the negative statements about asexuality. Many of the individual difference variables correlated moderately with the ATA. Multiple regression analyses indicated that significant predictors of the ATA included right-wing authoritarianism, internal motivation to respond without prejudice, intergroup disgust sensitivity, benevolent sexism, participant sexual orientation, and religiosity. Together, these six predictors accounted for half of the variance in the ATA. The findings of this study suggest that attitudes toward asexuality are similarly predicted by those individual difference variables that predict attitudes toward gays, lesbians, bisexuals, and transpersons.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".