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Making progress in global health: the need for new paradigms

2009· article· en· W2146901930 on OpenAlexaff
Solomon R. Benatar, Stephen Gill, Isabella Bakker

Bibliographic record

VenueInternational Affairs · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsYork University
Fundersnot available
KeywordsValue (mathematics)IntrospectionPolitical scienceGlobal healthPosition (finance)State (computer science)Law and economicsSpeculationPoliticsEngineering ethicsHealth careSociologyEconomicsLawPsychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

This article takes the state of health in the world today as the starting point for a backward look at the trajectory that has led to our current position and speculation about prospects for improved global health in the future. Our model of social development and its dominant value system, which has promoted scientific progress but has also brought about great social, economic and health instability, is interrogated. This leads to questions such as what it means to be healthy and what the practice of medicine is about. Three potential scenarios for global health in the future are outlined. It is suggested that deep introspection about our current value system is required to achieve a paradigm shift that could reverse current trends and lead both to improvements in health globally and to less human insecurity. The authors conclude that while we have the material resources to achieve ambitious goals we may lack the moral and political will to do so. An expanded discourse on ethics and human rights—as well as on the limits of what is politically possible— may provide the impetus to drive change towards an improved global economic system and better health globally.

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)0.089
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.089
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.004
Science and technology studies0.0140.130
Scholarly communication0.0390.093
Open science0.0060.025
Research integrity0.0210.042
Insufficient payload (model declined to judge)0.0100.003

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 head0.120
GPT teacher head0.514
Teacher spread0.394 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations103
Published2009
Admission routes1
Has abstractyes

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