Geographies of Medical and Health Humanities: A Cross-Disciplinary Conversation
Bibliographic record
Abstract
In recent years, both within and beyond academic and clinical spheres, medical and health humanities have become increasingly influential.Drawing from interdisciplinary fields in the humanities, social sciences, and the arts, medical and health humanities present unique lenses for considering nuanced spaces and lived experiences of health and health care; they also help challenge traditional ways that medicine and health care are understood and practiced.This collection brings together practitioners and theorists working broadly in medical health humanities, asking them both to consider their work as temporally and spatially located and to position their practices in conversation with a growing uptake of humanities methods and methodologies in other disciplines.The work of nine contributors uses these themes as a starting point for thinking about the future of medical health humanities in new and potentially even more productive ways.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.036 | 0.080 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".