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Arguments in Health Geography: On Sub‐Disciplinary Progress, Observation, Translation

2012· article· en· W2136897854 on OpenAlexaff
Gavin J. Andrews, Joshua Evans, James R. Dunn, Jeffrey R. Masuda

Bibliographic record

VenueGeography Compass · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of ManitobaAthabasca UniversityMcMaster University
Fundersnot available
KeywordsSituatedDisciplineHuman geographyPublic healthTheme (computing)Critical geographyHealth geographyHealth careStrategic geographySociologyHistorical geographySocial scienceEpistemologyHealth policyInternational healthPolitical scienceMedicineNursingLaw

Abstract

fetched live from OpenAlex

Abstract To introduce the sub‐discipline of health geography and its developing interests, this paper initially reviews the different forms of arguments mounted by researchers. First, arguments on the nature and progress of inquiry that speak to directions, concepts, theories and methods. Second, using health care settings, public health and environmental health as illustrations, arguments that interpret and explain health and health care in different ways. A final series of discussions takes the theme of arguments further in terms of how they might affect change in the world. Specifically, health geography is situated within four broad movements currently unfolding in the larger disciplines to which it contributes. With regard to the parent discipline of human geography, the ‘policy turn’ and more generally the idea of ‘public geography’. With regard to the health sciences, Evidenced‐Based Health Care and Knowledge Translation.

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)0.107
metaresearch head score (Gemma)0.152
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 score0.107
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.152
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0070.103
Scholarly communication0.0200.033
Open science0.0020.016
Research integrity0.0090.016
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 head0.062
GPT teacher head0.362
Teacher spread0.301 · 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

Citations37
Published2012
Admission routes1
Has abstractyes

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