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Record W2074051087 · doi:10.1093/phe/php032

Two Models in Global Health Ethics

2009· article· en· W2074051087 on OpenAlexaff
Christopher S. Lowry, Udo Schüklenk

Bibliographic record

VenuePublic Health Ethics · 2009
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsQueen's University
FundersNational University of Singapore
KeywordsReceiptPoliticsState (computer science)Global healthContext (archaeology)Political scienceSociologyLaw and economicsLawHealth careEconomicsComputer science

Abstract

fetched live from OpenAlex

This paper examines two strategies aimed at demonstrating that moral obligations to improve global health exist. The ‘humanitarian model’ stresses that all human beings, regardless of affluence or global location, are fundamentally the same in terms of moral status. This model argues that affluent global citizens’ moral obligations to assist less fortunate ones follow from the desirability of reducing disease and suffering in the world. The ‘political model’ stresses that the lives of the world's rich and poor are inextricably linked because of harmful state-to-state actions and because of the currently existing transnational institutions. These institutions’ design at once secures the high standard of living of the affluent and reinforces the continued foreseeable—and avoidable—deprivation of many of the global poor; and these give rise to compensatory health-related moral obligations beyond borders. This paper argues that political reasoning is unsuitable for the crucial task of determining priority in the receipt of health aid. We conclude that in the context of global health ethics, political reasoning must be supplemented with, if not replaced by, humanitarian reasoning.

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)0.012
metaresearch head score (Gemma)0.011
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.033
Scholarly communication0.0090.010
Open science0.0020.006
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0110.001

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 head0.510
GPT teacher head0.645
Teacher spread0.135 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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