Understanding Repugnance: Implications for Public Policy
Bibliographic record
Abstract
Understanding the influence of moral repugnance on social decisions is challenging, particularly because in several cases not all of the relevant policy options can be observed. In a series of recent studies, we designed survey experiments to identify individual preferences in morally controversial transactions, with focus on the provision of payments to kidney donors in the United States (Elias, Lacetera, & Macis, 2015a, 2015b, 2016a). We found that providing information on how a price mechanism can help alleviate the organ shortage significantly reduces opposition toward payments for organs. Moreover, we quantified the trade-off that people make between the repugnance and the efficiency of alternative kidney procurement systems. In Elias, Lacetera, Macis, and Salardi (2017), finally, we analyzed how the regulation of controversial activities is related to economic development. This paper summarizes these findings and analyzes their main implications for public policy and market design.
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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 | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".