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Defending The Indefensible: The Global Asbestos Industry and its Fight for Survival

2008· book· en· W1542419020 on OpenAlexaboutno aff
Jock McCulloch

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
Fundersnot available
KeywordsAsbestosGovernment (linguistics)Political scienceMesotheliomaArgument (complex analysis)LegislationReputationBusinessEconomic growthLawMedicineEconomics

Abstract

fetched live from OpenAlex

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.

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 categoriesResearch integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.455
Threshold uncertainty score0.995

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 integrity0.0140.007
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.278
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2008
Admission routes1
Has abstractyes

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