Do attitude functions and perceiver demographics predict attitudes towards asexuality?
Bibliographic record
Abstract
Research indicates asexual individuals experience stigma. Addressing this phenomenon, this study examined attitude functions – experiential, social-expressive, ego-defensive, and value-expressive – in the prediction of attitudes towards asexuality. As well, demographic variables – participant gender, religiosity, and sexual orientation – were examined vis-à-vis asexuality attitudes. Herek’s Function of Attitudes Inventory assessed asexual attitude functions. General attitudes were assessed using the Attitude towards Asexuality scale, feeling thermometers, and semantic differential scales. Participants were asked to imagine developing a relationship with an asexual person; attitudes towards the asexual target were assessed by belief statements specific to the person, a feeling thermometer, and target-specific semantic differential items. On average, all asexuality attitudes measures were rated favourably. Men, religious individuals, and exclusively heterosexual participants were generally less positive in their asexual attitudes. While statistically significant, these demographic differences were quite weak. Participants generally denied the attitude functions as the basis for their asexuality attitudes. The ego-defensive attitude function was strongly predictive of all asexual attitudes measures. The value-expressive function was a significant but small multiple regression predictor of some asexual attitudes. Understanding attitudes towards asexuality would be advanced by further consideration of how the attitude serves the social perceiver.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".