The politics of gaydar: Ideological differences in the use of gendered cues in categorizing sexual orientation.
Bibliographic record
Abstract
In the present research, we investigated whether, because of differences in cognitive style, liberals and conservatives would differ in the process of categorizing individuals into a perceptually ambiguous group. In 3 studies, we examined whether conservatives were more likely than liberals to rely on gender inversion cues (e.g., feminine = gay) when categorizing male faces as gay vs. straight, and the accuracy implications of differential cue usage. In Study 1, perceivers made dichotomous sexual orientation judgments (gay-straight). We found that perceivers who reported being more liberal were less likely than perceivers who reported being more conservative to use gender inversion cues in their deliberative judgments. In addition, liberals took longer to categorize targets, suggesting that they may have been thinking more about their judgments. Consistent with a stereotype correction model of social categorization, in Study 2 we demonstrated that differences between liberals and conservatives were eliminated by a cognitive load manipulation that disrupted perceivers' abilities to engage in effortful processing. Under cognitive load, liberals failed to adjust their initial judgments and, like conservatives, consistently relied on gender inversion cues to make judgments. In Study 3, we provided more direct evidence that differences in cognitive style underlie ideological differences in judgments of sexual orientation. Specifically, liberals were less likely than conservatives to endorse stereotypes about gender inversion and sexual orientation, and this difference in stereotype endorsement was partially explained by liberals' greater need for cognition. Implications for the accuracy of ambiguous category judgments made with the use of stereotypical cues in naturalistic settings 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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".