When Intergroup Contact is Uncommon and Bias is Strong: The Case of Anti-Transgender Bias
Bibliographic record
Abstract
In contrast to the centrality of “coming out” in the gay rights movement, transgender people may be less likely to disclose their transgender status due to the severity of anti-transgender stigma, structural factors, and differences in how transgender status and sexual identity are expressed. As a consequence, intergroup contact with transgender people may be less common than gay contact, which may limit its effectiveness. In Study 1 (N = 174), transgender contact was much less frequent than gay contact, and transgender contact frequency was not associated with anti-transgender bias, although more positive transgender contact was associated with lower anti-transgender bias, and gay contact frequency was also independently associated with lower anti-transgender bias. In Study 2 (N = 277), greater transgender “media contact” was associated with increased empathy for transgender people and decreased anti-transgender bias. In addition, several participants left unsolicited anti-transgender comments at the end of the study, and these participants tended to have less transgender contact and were higher in Right-Wing Authoritarianism and Social Dominance Orientation. Our results suggest that increasing contact with the LGBT community and increasing media representations of transgender people may decrease anti-transgender bias. Future directions building on these results are discussed.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".