Gender Essentialism and the Mental Representation of Transgender Women and Men: A Multimethod Investigation of Stereotype Content
Bibliographic record
Abstract
Social category systems – such as nationality, class, and gender – are constantly shifting. How are emergent social groups, which had previously not been societally recognized, mentally represented alongside more established groups? The growing visibility of transgender women and men in the US is an opportunity to observe this sort of cultural shift. Across three diverse methods of stereotype measurement, we assessed characteristics associated with transgender women and men and compared them to the stereotypes of their more traditional (cisgender) counterparts. In our final study, we directly assessed how people mentally position transgender groups relative to culturally well-established cisgender groups. Across these four studies, we show that mental representations of emergent gender groups are highly idiosyncratic, such that some participants extended same-gender-identity stereotypes to transgender groups (e.g., stereotyping transgender women as feminine), while others extended same-sex-assigned-at-birth stereotypes to transgender groups (e.g., stereotyping transgender women as masculine). Moreover, these differences were substantially explained by endorsement of gender essentialist beliefs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".