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Record W199829465 · doi:10.18584/iipj.2014.5.3.1

Health Care as Commons: An Indigenous Approach to Universal Health Coverage

2014· article· en· W199829465 on OpenAlexvenueno aff
Young Soon Wong, Pascale Allotey, Daniel D. Reidpath

Bibliographic record

VenueInternational Indigenous Policy Journal · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousHealth careGovernment (linguistics)Population healthCommonsHealth equityPopulationEconomic growthState (computer science)Health policyPolitical scienceBusinessMedicineEnvironmental healthLawEconomicsEcologyComputer science

Abstract

fetched live from OpenAlex

Modern health care systems of today are predominantly derived from Western models and are either state owned or under private ownership. Government, through their health policies, generally aim to facilitate access for the majority of the population through the design of their health systems. However, there are communities, such as Indigenous peoples, who do not necessarily fall under the formal protection of state systems. Throughout history, these societies have developed different ways to provide health care to its population. These health care systems are held and managed under different property regimes with their attendant advantages and disadvantages. This article investigates the gaps in health coverage among Indigenous peoples using the Malaysian Indigenous peoples as a case study. It conceptually examines a commons approach to health care systems through a study of the traditional health care system of indigenous peoples and suggests how such an approach can help close this gap in the remaining gaps of universal health coverage.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.014
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.002
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 head0.029
GPT teacher head0.320
Teacher spread0.291 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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