Affective and Attitudinal Features of Benevolent Heterosexism in Italy: The Italian Validation of the Multidimensional Heterosexism Inventory
Bibliographic record
Abstract
Abstract Introduction People who belong to a sexual and gender minority often face prejudices that have their roots in heterosexism, a sociocultural system that can manifest itself in different ways and sometimes in a seemingly benevolent fashion. The present study examined the psychometric properties of the Multidimensional Heterosexism Inventory (MHI), a scale assessing aversive, amnestic, paternalistic, and positive stereotypic heterosexism, in an Italian sample. Methods Two hundred one cisgender and heterosexual individuals (129 women and 72 men) aged 18 to 81 years (M = 36.42, SD = 12.56) were recruited online between May and October 2022 and answered questions about social dominance orientation, right-wing authoritarianism, ambivalent sexism, and attitudes toward lesbians and gay men. Results Confirmatory factor analysis showed that the original 4-factor model of the scale fit the data well. Predictive and convergent validity of the Italian version of the MHI was adequate, whereas discriminant validity was not fully achieved due to overlap of multidimensional heterosexism with hostile and benevolent sexism and authoritarianism. Scores were higher for aversive and amnesic heterosexism in men than in women, but not for paternalistic and positive stereotypic heterosexism. Finally, less educated participants, those with no LGBTQI + friends, and religious participants were higher in all MHI subscales than their counterparts. Conclusions This study provides the first evidence for the validity and reliability of an Italian version of the MHI. Policy Implications Using the MHI can help to make visible not only the explicit but also the subtle forms of heterosexism, thus recognizing the multidimensional nature of heterosexism produced in social institutions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".