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Record W2801820285 · doi:10.3386/w24572

Moral NIMBY-ism? Understanding Societal Support for Monetary Compensation to Plasma Donors in Canada

2018· report· en· W2801820285 on OpenAlexaffabout
Nicola Lacetera, Mario Macis

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typereport
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNIMBYCompensation (psychology)Political scienceEconomicsSocial psychologyLaw and economicsEnvironmental ethicsPsychologyEcologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

The growing demand for plasma, especially for the manufacture of therapeutic products, prompts discussions on the merits of different procurement systems. We conducted a randomized survey experiment with a representative sample of 826 Canadian residents to assess attitudes toward legalizing payments to plasma donors, a practice that is illegal in several Canadian provinces. We found no evidence of widespread societal opposition to payments to plasma donors. On the contrary, over 70% of respondents reported that they would support compensation. Our Canadian respondents were more in favor of paying plasma donors elsewhere than in Canada, but the differences were small, suggesting a weak role for moral "NIMBY-ism" or relativism. Moral concerns were the respondents' main reason for opposing payments, together with concerns for the safety of plasma from compensated donors, although most of the plasma in Canada does come from paid U.S. donors. Among those in favor of legalizing payments to donors, the main rationale was to guarantee a higher domestic supply. Finally, roughly half of those who declared to be against payments reported that they would reconsider their position if domestic supply plus imports did not cover domestic demand. Most Canadians, therefore, seem to espouse a consequentialist view on issues related to the procurement of plasma.

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 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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.467
GPT teacher head0.503
Teacher spread0.036 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2018
Admission routes2
Has abstractyes

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