Defending The Indefensible: The Global Asbestos Industry and its Fight for Survival
Bibliographic record
Abstract
Abstract In the early twentieth century, asbestos had a reputation as a lifesaver. In 1960, however, it became known that even relatively brief exposure to asbestos can cause mesothelioma, a virulent and lethal cancer. Yet the bulk of the world’s asbestos was mined after 1960. Asbestos usage in many countries continued unabated. This is the first global history of how the asbestos industry and its allies in government, insurance, and medicine defended the product throughout the twentieth century. It explains how mining and manufacture could continue despite overwhelming medical evidence as to the risks. The argument advanced in this book is that asbestos has proved so enduring because the industry was able to mount a successful defense strategy for the mineral - a strategy that still operates in some parts of the world. This defence involved the shaping of the public debate by censoring, and sometimes corrupting, scientific research, nurturing scientific uncertainty, and using allies in government, insurance, and medicine. The book also discusses the problems of asbestos in the environment, compensating victims, and the continued use of asbestos in the developing world. Its global focus shows how asbestos can be seen as a model for many occupational diseases - indeed for a whole range of hazards produced by industrial societies. The book is based on a wealth of documentary material gained from legal discovery, supplemented by evidence from the authors’ visits and researches in the US, the UK, Canada, Kazakhstan, Zimbabwe, Australia, Swaziland, and South Africa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".