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Record W3023717889 · doi:10.3386/w22673

Incentives and Ethics in the Economics of Body Parts

2016· report· en· W3023717889 on OpenAlexaff
Nicola Lacetera

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typereport
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIncentiveEconomicsPublic economicsLaw and economicsMicroeconomicsBusiness

Abstract

fetched live from OpenAlex

Research shows that properly devised economic incentives increase the supply of blood without hampering its safety; similar effects may be expected also for other body parts such as bone marrow and organs.These positive effects alone, however, do not necessarily justify the introduction of payments for supplying body parts; these activities concern contested commodities or repugnant transactions, i.e. societies may want to prevent certain ways to regulate a transaction even if they increased supply, because of ethical concerns.When transactions concern contested commodities, therefore, societies often face trade-offs between the efficiencyenhancing effects of trades mediated by a monetary price, and the moral opposition to the provision of these payments.In this essay, I first describe and discuss the current debate on the role of moral repugnance in controversial markets, with a focus on markets for organs, tissues, blood and plasma.I then report on recent studies focused on understanding the trade-offs that individuals face when forming their opinions about how a society should organize certain transactions.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.025
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.498
GPT teacher head0.549
Teacher spread0.051 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes1
Has abstractyes

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