Social categorization and stereotyping: ‘You mean I'm one of “them”?’
Bibliographic record
Abstract
What happens when people discover that they are members of a group about which they have previously formed some stereotype? To address this question, procedures previously shown to induce negative stereotypes of minority groups were combined with a social categorization manipulation. Participants in a distinctiveness-based illusory correlation paradigm (Hamilton & Gifford, 1976) either knew nothing about their group membership, or learned that they were a member of the minority group or the majority group either before or after being presented the stereotype-engendering stimulus materials. Results revealed that social categorization into the minority group before stimulus presentation eliminated the perceived stereotype and reversed the evaluative bias, whereas social categorization into the minority group after stimulus presentation had no effect on the perceived stereotype and only a weak effect in reducing the evaluative bias. Social categorization into the majority group either before or after stimulus presentation had little effect on the perceived stereotype and evaluative bias. These results clarify the process underlying the influence of social categorization on stereotype formation, underscore the distinction between affective and cognitive influences on stereotype formation and stereotype change, and offer insights into 'autostereotyping' among members of minority groups.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".