Moral NIMBY-ism? Understanding Societal Support for Monetary Compensation to Plasma Donors in Canada
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".