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Record W2340728505 · doi:10.1177/0020731416631734

Global Health Governance and Global Power

2016· article· en· W2340728505 on OpenAlexaff
Stephen Gill, Solomon R. Benatar

Bibliographic record

VenueInternational Journal of Health Services · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsYork University
Fundersnot available
KeywordsGlobal healthCorporate governancePower (physics)Global governancePolitical scienceBusinessEconomic growthEconomicsHealth careFinance

Abstract

fetched live from OpenAlex

The Lancet-University of Oslo Commission Report on Global Governance for Health provides an insightful analysis of the global health inequalities that result from transnational activities consequent on what the authors call contemporary "global social norms." Our critique is that the analysis and suggested reforms to prevailing institutions and practices are confined within the perspective of the dominant-although unsustainable and inequitable-market-oriented, neoliberal development model of global capitalism. Consequently, the report both elides critical discussion of many key forms of material and political power under conditions of neoliberal development and governance that shape the nature and priorities of the global governance for health, and fails to point to the extent of changes required to sustainably improve global health. We propose that an alternative concept of progress-one grounded in history, political economy, and ecologically responsible health ethics-is sorely needed to better address challenges of global health governance in the new millennium. This might be premised on global solidarity and the "development of sustainability." We argue that the prevailing market civilization model that lies at the heart of global capitalism is being, and will further need to be, contested to avoid contradictions and dislocations associated with the commodification and privatization of health.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.611
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.341
Teacher spread0.327 · 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

Citations90
Published2016
Admission routes1
Has abstractyes

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