Dialectical thinking and fairness-based perspectives of affirmative action.
Bibliographic record
Abstract
Affirmative action (AA) policies are among the most effective means for enhancing diversity and equality in the workplace, yet are also often viewed with scorn by the wider public. Fairness-based explanations for this scorn suggest AA policies provide preferential treatment to minorities, violating procedural fairness principles of consistent treatment. In other words, to promote equality in the workplace, effective AA policies promote inequality when selecting employees, and the broader public perceives this to be procedurally unfair. Given this inconsistency underlies negative reactions to AA policies, we argue that better preparing individuals to deal with inconsistencies can mitigate negative reactions to AA policies. Integrating theories from the fairness and cognitive styles literature, we demonstrate across 4 studies how dialectical thinking-a cognitive style associated with accepting inconsistencies in one's environment-increases support for AA policies via procedural fairness perceptions. Specifically, we found support for our propositions across a variety of AA policy types (i.e., strong and weak preference policies) and when conceptualizing dialectical thinking either as an individual difference or as a state that can be primed-including being primed by the framing of the AA policy itself. We discuss theoretical contributions and insights for policy-making at government and organizational levels. (PsycINFO Database Record
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".