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Record W4387563726 · doi:10.1177/17579759231194600

Defining health through a critical materialist political economy lens

2023· article· en· W4387563726 on OpenAlexaff
Stella Medvedyuk, Dennis Raphael

Bibliographic record

VenueGlobal Health Promotion · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsYork University
Fundersnot available
KeywordsMaterialismPoliticsHealth policyHealth equityPublic relationsEnvironmental ethicsPolitical scienceSociologyPositive economicsEpistemologyHealth careEconomicsLaw

Abstract

fetched live from OpenAlex

It has been recognized since antiquity that the organization of society and how it distributes resources are the primary determinants of health. Yet most definitions of health in the academic and practice literatures limit their focus to the individual's experience of health and functional abilities, neglecting the structures and processes of societies in which the individual is embedded. We draw upon developments in the critical health communication and critical materialist political economy of health literatures to provide a definition of health that directs attention to the role that economic and political systems play in either equitably or inequitably distributing the resources necessary for health. Since these distributions interact with the individual's unique biological and psychological dispositions and situations to produce health, it is important to identify their sources and means of making their distributions more equitable. Because it is through communication that humans interpret society, themselves, and others, a concise definition of health that draws attention to these societal features and their roles on a day-to-day basis in promoting or threatening health is essential.

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)0.010
metaresearch head score (Gemma)0.009
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 score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0060.063
Scholarly communication0.0120.011
Open science0.0010.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.101
GPT teacher head0.409
Teacher spread0.308 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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