Bibliographic record
Abstract
BACKGROUND: Twenty years after the start-up of the Canadian asbestos industry, reports began to appear of respiratory disease and deaths in asbestos workers in England and in France. An inquiry from the UK in 1912 as to the health of Quebec miners was met by a denial of ill-health, but the loading of the premiums of asbestos workers in the 1930s indicated that, despite further reassuring health studies on Quebec miners, actuaries had data that gave cause for serious concern. METHODS: A report made to the Canadian asbestos industry by a company doctor in 1940, reviewing the literature and presenting his health findings on some 500 employees, was studied in the context of the published information available at the time, and of unpublished contemporaneous material subsequently obtained by legal discovery. RESULTS: The physician denied that the health and longevity of Quebec's miners and millers were adversely affected, and was dismissive of earlier reports of there being serious health risks associated with working with asbestos. CONCLUSIONS: The methodology employed in his health study was defective and his denial of the literature uninformed. The study was widely circulated, and while it may have boosted Canadian industry morale, it met with a sceptical response from British industry. In denying that conditions in Quebec's asbestos mines and mills disabled and killed workers, the author allied himself to fellow professionals loyal to Government and to industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".