Sacred Values? The Effect of Information on Attitudes toward Payments for Human Organs
Bibliographic record
Abstract
Many economic transactions are prohibited-even in the absence of health or safety concerns or negative externalities-because of ethical concerns that cause these exchanges to be perceived as "repugnant" if conducted through a market.Establishing a system of payments for human organs is a particularly relevant example given its implications for public health; in almost all countries, these payments are prohibited because they are considered morally unacceptable-a prohibition that societies seem to accept despite the long waitlists and high death rates for people needing a transplant.We investigate how deeply rooted these attitudes are and, in particular, whether providing information on how a price mechanism can help alleviate the organ shortage changes people's opinions about the legalization of these transactions.We conducted a survey experiment with 3,417 subjects in the U.S. and found that providing information significantly increased support for payments for organs from a baseline of 52% to 72%, and this increase applied to most of the relevant subgroups of the analyzed sample.Additional analyses on the support for other morally controversial activities show that attitude changes in response to information depend on the type of activity under consideration and interactions with other beliefs.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".