Attitudes on Affirmative Action in University Students: Effects of Race, Political Beliefs and Prejudice
Bibliographic record
Abstract
The interplay between explicit and implicit attitudes toward affirmative action (AA) policies is relevant to applied psychology. Its comprehension helps to improve our capacity to evaluate support for such policies. The purpose of this study was to determine the extent to which students’ race, political opinion of affirmative action, and prejudice against minorities influence the relationship between implicit-explicit attitudes toward affirmative action policies. 492 student participants were recruited from a large Brazilian public university about racial quotas in admissions. Implicit and explicit measures of attitude about the admission process were applied, together with measures of political opinion of affirmative action, prejudice against minorities and race. The results show that race has little effect on the difference between implicit and explicit attitudes about the admission process, but that prejudice and political position exert strong effects. Our findings suggest that implicit measures of attitudes should be used when evaluating attitudes on AA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".