Prejudice, Political Ideology, and Interest: Understanding Attitudes Toward Affirmative Action in Brazil
Bibliographic record
Abstract
Few public policies have been as consequential or divisive as affirmative action. Proponents have argued for the need for equity and the redress of past and present discrimination, whereas opponents enlist claims over individual liberty and merit. Scholars have examined support to affirmative action, asking to what extent citizens' support is shaped by their political ideology, interest, prejudice, or some combination thereof. Much work to date has focused on the United States, where disentangling theoretical explanations has proved challenging. We turn our attention to an understudied but important case: Brazil. Brazil has implemented a broad form of affirmative action for admission to federal universities that include consideration of the applicant's education, income, and race. Adopting both a conventional question and a list experiment embedded in a face‐to‐face survey among a nationally representative sample of adult Brazilians, we find that public support for affirmative action suffers from social desirability bias, and in our subsequent regression analysis, that attitudes about affirmative action are structured especially by individuals' interests.
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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 | 0.013 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".