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Record W2986321573 · doi:10.1080/10402659.2019.1667560

Approaching Peace Through Health with a Critical Eye

2019· article· en· W2986321573 on OpenAlexaboutno aff
Neil Arya

Bibliographic record

VenuePeace Review · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeacebuildingPrivilege (computing)Health policyPolitical sciencePublic relationsBridge (graph theory)Public administrationSociologyHealth careMedicineLaw

Abstract

fetched live from OpenAlex

It has been a privilege to be asked to edit this edition of Peace Review and to see such interests in the connections between peace and health. Peace through Health (PtH) was developed at McMaster University in Canada in the 1990s as a theoretical concept with practical applications such as field projects. Building on the Health as a Bridge to Peace (HBP) policy and planning framework of the Pan American Health Organization (PAHO) and the World Health Organization (WHO) in the 1980s, the framework encouraged collaboration on policy development, training, and service delivery across borders and lines in conflict, integrating peacebuilding “concerns, concepts, principles, strategies, and practices into health relief and health sector development.”

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)0.017
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0040.009
Scholarly communication0.0130.015
Open science0.0020.005
Research integrity0.0080.015
Insufficient payload (model declined to judge)0.0060.002

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 head0.126
GPT teacher head0.525
Teacher spread0.399 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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