Bibliographic record
Abstract
In 1937, Dr Alan Brown, the famed Toronto paediatrician, played a critical role in stimulating the passage of laws mandating the pasteurization of milk in Ontario. He had challenged the Premier of the day, Mitchell Hepburn, to introduce such legislation during the premier's visit to The Hospital for Sick Children, Toronto, Ontario. The premier had been struck by the plight of the many children suffering from the effects of bovine tuberculosis. Brown recognized that political action, not clinical interventions, would eliminate the scourge of bovine tuberculosis. Hepburn's announced intention to pass such laws ignited a political firestorm as the powerful farm lobby of the day swung into gear in an attempt to prevent the passage of this legislation. It is a tribute to Dr Brown's insight, and Mitchell Hepburn's tenacity and determination that the law was eventually passed, and Ontario's children received the benefits of pasteurized milk. It was, in essence, a dramatic example of the impact of the development of ‘healthy’ public policy and the important role that physicians can play in precipitating and supporting such initiatives (1). Political advocacy in the cause of public health is as appropriate today as it was in Dr Brown's era, and it is in keeping with the finest traditions of professional behaviour.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".