A Matter of Principle or Self-Interest? Examining Support for Affirmative Action
Bibliographic record
Abstract
Recent polls have found that support for affirmative action in the United States is divided largely along political lines, with liberals generally supporting it, and conservatives generally opposing it.However, with conservatives being overwhelmingly White, and affirmative action policies generally designed to benefit racial and ethnic minorities, it is unknown how much of peoples' support is motivated by political principle, or grouplevel self-interest.I attempted to empirically test this question by subjecting participants to one of four affirmative action policies, differing only on the proposed beneficiary (viz.liberal, conservative, Black, White), and measuring the influence of both principle (via political affiliation) and self-interest (via group congruence).I hypothesized that people would reveal themselves to be motivated by self-interest, with potential moderators (viz.threat and strength of group identification).I found that both principle and self-interest predict support for affirmative action.Implications for affirmative action policies 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".