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Record W2928216516 · doi:10.7202/1058139ar

Paying for Plasma: Commodification, Exploitation, and Canada's Plasma Shortage

2019· article· en· W2928216516 on OpenAlexaffvenueabout
Vida Panitch, L. Chad Horne

Bibliographic record

VenueCanadian Journal of Bioethics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCarleton University
Fundersnot available
KeywordsCommodificationPaymentProfit motiveDonationEconomic shortageProfit (economics)Agency (philosophy)BusinessEconomicsMarket economySociologyEconomic growthFinanceMicroeconomicsSocial science

Abstract

fetched live from OpenAlex

A private, for-profit company has recently opened a pair of plasma donation centres in Canada, at which donors can be compensated up to $50 for their plasma. This has sparked a nation-wide debate around the ethics of paying plasma donors. Our aim in this paper is to shift the terms of the current debate away from the question of whether plasma donors should be paid and toward the question of who should be paying them. We consider arguments against paying plasma donors grounded in concerns about exploitation, commodification, and the introduction of a profit motive. We find them all to be normatively inconclusive, but also overbroad in light of Canada’s persistent reliance on plasma from paid donors in the United States. While we believe that there are good reasons to oppose allowing a private company to profit from Canada’s blood supply, these concerns can be addressed if payment is dispensed instead by a public, not-for-profit agency. In short, we reject profiting from plasma while we endorse paying for plasma; we therefore conclude in favour of a new Canadian regime of public sector plasma collection and compensation.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.013
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.254
Teacher spread0.208 · 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.

Study designQualitative
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

Citations12
Published2019
Admission routes3
Has abstractyes

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