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Health geography in Canada: where are we headed?

2009· article· en· W2085530586 on OpenAlexafffundvenueabout
Isaac Luginaah

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern University
FundersUniversité de Sherbrooke
KeywordsHealth geographyRestructuringHuman geographyPublic healthWork (physics)PoliticsField (mathematics)Health policySocial geographySociologyHealth careSocial sciencePolitical scienceInternational healthMedicine

Abstract

fetched live from OpenAlex

This paper overviews the emergence of medical/health geography in Canada. The paper discusses the key questions that Canadian health geographers have explored in the past two decades, how these enquiries have featured in the field and how they contribute to the wider discourse of human geography. It also addresses questions on emerging themes and where Canadian health geography will go in the years ahead. With shifting health landscapes in terms of changes in social, political and physical environments, and changes in health care restructuring, Canadian health geographers are entering a new phase of research, teaching and policy. The complexity of the questions that health geographers seek to address means it is necessary to continue to highlight the policy implications of their findings. Health geographers need to emphasize the public agenda through interdisciplinary research and by continuing to work with geographers in other subfields.

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)0.006
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.862

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.014
Science and technology studies0.0320.028
Scholarly communication0.0210.010
Open science0.0030.009
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.014
GPT teacher head0.247
Teacher spread0.232 · 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
GenreReview

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

Citations33
Published2009
Admission routes4
Has abstractyes

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