Faire l’histoire de la médicalisation en Chine du Sud au début du XXe siècle : l’expérience des sources
Bibliographic record
Abstract
À partir du cas d’étude de la province méridionale chinoise du Guangdong, cet article interroge la tendance marquée de l’historiographie à aborder le phénomène de la médicalisation en Chine au début du XXe siècle à partir des cas de Beijing et Shanghai. En révélant les limites et les biais que présentent les archives de la Rockefeller Foundation, incontournables des travaux de recherches sur le sujet, il soulève l’idée selon laquelle ces sources auraient pu contribuer à infléchir la démarche des chercheurs et à entretenir un certain désintérêt pour les initiatives médicalisatrices dans les régions chinoises excentrées comme le Guangdong.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".