The Case for a Global Ban on Asbestos
Bibliographic record
Abstract
BACKGROUND: All forms of asbestos are now banned in 52 countries. Safer products have replaced many materials that once were made with it. Nonetheless, many countries still use, import, and export asbestos and asbestos-containing products, and in those that have banned other forms of asbestos, the so-called "controlled use" of chrysotile asbestos is often exempted from the ban. In fact, chrysotile has accounted for > 95% of all the asbestos used globally. OBJECTIVE: We examined and evaluated the literature used to support the exemption of chrysotile asbestos from the ban and how its exemption reflects the political and economic influence of the asbestos mining and manufacturing industry. DISCUSSION: All forms of asbestos, including chrysotile, are proven human carcinogens. All forms cause malignant mesothelioma and lung and laryngeal cancers, and may cause ovarian, gastrointestinal, and other cancers. No exposure to asbestos is without risk. Illnesses and deaths from asbestos exposure are entirely preventable. CONCLUSIONS: All countries of the world have an obligation to their citizens to join in the international endeavor to ban the mining, manufacture, and use of all forms of asbestos. An international ban is urgently needed. There is no medical or scientific basis to exempt chrysotile from the worldwide ban of asbestos.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".