What makes affirmative action‐based hiring decisions seem (un)fair? A test of an ideological explanation for fairness judgments
Bibliographic record
Abstract
Summary Studies show that Whites tend to show the lowest level of support for affirmative action (AA) policies. Opponents of AA often argue that this is because it violates principles of meritocracy. However, self‐interest (based on social identification with those adversely affected) could also explain their opposition. In three studies, we varied whether an Asian or White male is adversely affected by AA to test another explanation; namely, that Whites' fairness judgments are based on both the adversely affected person's race and the fairness evaluator's ideological beliefs. Although we found some support for the meritocratic explanation, this was not sufficient to explain why Whites view AA as (un)fair. Instead, we found strong support for our prediction that Whites who are opposed to equality perceive more unfairness when a White (vs. Asian) was harmed by AA, whereas Whites who endorse egalitarian ideologies perceive the opposite. This finding suggests that neither self‐interest nor meritocratic explanations can fully account for Whites' opposition to AA. Copyright © 2014 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".