Arguments in Health Geography: On Sub‐Disciplinary Progress, Observation, Translation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.103 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".