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Record W2551155338 · doi:10.1177/1048291116679951

How Canada’s Asbestos Industry Was Defeated in Quebec

2016· article· en· W2551155338 on OpenAlexaboutno aff
Kathleen Ruff

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2016
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsbestosChrysotileVictoryGovernment (linguistics)Political sciencePoliticsSolidarityPublic administrationEconomic growthLawEconomics

Abstract

fetched live from OpenAlex

Less than a decade ago, the Quebec asbestos industry enjoyed support from all the political parties in the Canadian House of Commons and the Quebec National Assembly, as well as from business and union organizations. Two lobby organizations (Chrysotile Institute and International Chrysotile Association) had significant global impact in promoting asbestos use and defeating asbestos ban efforts in developing countries. Quebec's two asbestos mines planned to expand operations and make Quebec the second biggest global asbestos exporter. With the aid of lobbyists, public relations consultants, and government financing, the asbestos industry came close to succeeding. The article examines how a campaign of international solidarity, involving scientific experts, asbestos victims, and health activists in Quebec, Canada, and overseas, succeeded in closing the two mines and defeating the political and social power that the Quebec asbestos industry had wielded for a century. This victory ended Canada's destructive role as global propagandist for the asbestos industry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.891

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0310.008
Scholarly communication0.0100.002
Open science0.0020.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.024
GPT teacher head0.293
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations5
Published2016
Admission routes1
Has abstractyes

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Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicOccupational and environmental lung diseasesFrench-language works237,207