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Record W2765144685 · doi:10.1002/wmh3.246

Understanding Repugnance: Implications for Public Policy

2017· article· en· W2765144685 on OpenAlex
Julio Elías, Nicola Lacetera, Mario Macis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Medical & Health Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Toronto
FundersJohns Hopkins University
KeywordsOpposition (politics)PaymentEconomic shortageProcurementPublic economicsEconomicsLaw and economicsPolitical scienceBusinessLawMarketingFinancePolitics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.408
GPT teacher head0.548
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it