Misplaced Intuitions in Interventions to Reduce Attractiveness-Based Discrimination
Bibliographic record
Abstract
Individuals and organizations are increasing efforts to address discrimination. Nonexperts may lack awareness of, or are resistant to, scientifically informed strategies for reducing discrimination, instead relying on intuition. Five studies investigated the accuracy of nonexperts’ intuitions about reducing discrimination concerning physical attractiveness. In Studies 1a to 1c ( N = 902), participants predicted the effectiveness of six interventions to reduce attractiveness-based favoritism on a judgment task. Studies 2a and 2b ( N = 6,292) investigated the effectiveness of these interventions. Although two interventions reduced discrimination, intuitions were poorly aligned with actual results; fewer than 1% of participants identified the combination of interventions that did, versus did not, impact judgment, and responses were more likely to be below than above chance when predicting each intervention’s effectiveness. Although follow-up work should investigate the accuracy of intuition in other forms of discrimination, these results further stress the need for greater development and adoption of evidence-based strategies for combating discrimination.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".