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Record W4394763953 · doi:10.1017/mdh.2024.6

Crafting British medicine in the Empire: the establishment of medical schools in India and Canada, 1763–1837

2024· article· en· W4394763953 on OpenAlexfundaboutno aff
Martin Robert

Bibliographic record

VenueMedical History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural, Psychoanalytic, and Sociopolitical Reflections
Canadian institutionsnot available
FundersWolfson College, University of OxfordSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureUniversity of Oxford
KeywordsEmpireJurisdictionBritish EmpireApprenticeshipCurriculumCompetence (human resources)MedicineHistory of medicineMedical educationAncient historyLawHistoryPolitical scienceClassicsManagement

Abstract

fetched live from OpenAlex

In the early nineteenth century, medical schools became a growing means of regulating medicine in the British Empire, both in the metropole and in two colonies: India and Canada. By examining the establishment of medical schools in Calcutta, Bombay, Madras, Quebec City, Montreal and Toronto between the end of the Seven Years' War and the beginning of the Victorian era, this article argues that the rise of the British Empire was a key factor in the gradual replacement of private medical apprenticeships with institutional medical education. Although the imperial state did not implement a uniform medical policy across the British Empire, the medical schools established under its jurisdiction were instrumental in devising a curriculum that emphasised human dissection, bedside training in hospitals and organic chemistry as criteria of medical competence.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0200.016
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.339
Teacher spread0.313 · 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.

Study designQualitative
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

Citations2
Published2024
Admission routes2
Has abstractyes

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