“It Wasn’t Meant for Gays”: Lesbian Women’s and Gay Men’s Reactions to the Ambivalent Sexism Inventory
Bibliographic record
Abstract
Abstract Researchers can unintentionally reinforce societal prejudice against minoritized populations through the false assumption that psychological measurements are generalizable across identities. Recently, researchers have posited that gender and sexually diverse (GSD) people could feel excluded or confused by the Ambivalent Sexism Inventory (ASI) due to its overtly heteronormative statements like “A man is incomplete without the love of a woman.” Yet, the ASI is used for indexing the endorsement of sexism in GSD samples and across diverse populations. An ideal test of these experiences is to directly consult GSD participants for their reactions. In the current study, we report a reflexive thematic analysis of lesbian women and gay men’s (N = 744) feedback immediately after completing the ASI. Four themes characterized participants’ reactions to the ASI: Exclusion: Heteronormative items erase diverse genders and sexualities, Confusion: Inability to meaningfully respond due to heteronormativity, Hope: Exclusion understood as a necessary sacrifice toward progress, and Distress: Exclusion inflicts distress by reflecting societal prejudice. The themes captured the experience that many participants found heteronormative assumptions salient in the ASI and had varied reactions to the heteronormativity. Our results extend prior research that questions the generalizability of results drawn from the ASI, especially studies including GSD participants. We discuss the implications of the continued use of the ASI and encourage researchers to critically evaluate underlying theories and assumptions to ensure participants can engage with measures as intended.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".