MétaCan
Menu
Back to cohort

Northern Lights: London and Toronto

2003· book-chapter· en· W4388334095 on OpenAlexaboutno aff
John E. Farley

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistory of Medicine and Tropical Health
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismHygienePolitical scienceHistoryMedicineEconomic historyLaw

Abstract

fetched live from OpenAlex

Abstract In the 1920s, the Health Board endowed two schools of hygiene, one in London and the other in Toronto, which they regarded as model institutions, sharing with Johns Hopkins the distinction of being centers of research with the very highest standards-and so recipients of by far the most funding. At the same time, it turned its back on Rio and hospital schools of nursing, deciding instead to endow university schools of nursing, none more important than that at the University of Toronto. The Brazilian experiments were not to be repeated. Initially, both London and Toronto were seen as schools of the British empire, the former the central home and the latter serving Canada and the British Caribbean. Once the idea that schools should be opened in the poorer countries had seemingly been abandoned, London and Toronto would have seemed the logical next step where colonial nationals could be trained in first-class institutions. But such a smooth transition did not occur. European schools that had no link with the tropics predated the Health Board’s endowments of London and Toronto, although the European schools were never to attain the same status as Toronto and London. And in the end, the Health Board seemed more concerned with opening schools of hygiene in London and Toronto to serve the health needs of the British and Canadians than in creating centers of colonial training. Clearly, the Health Board’s attention was turning northward away from the problems of the tropical and colonial countries where they had started.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.920
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0330.000

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 head0.025
GPT teacher head0.214
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicHistory of Medicine and Tropical HealthFrench-language works237,207