Misconceptions and Misuse of International Agency for Research on Cancer `Classification of Carcinogenic Substances'
Bibliographic record
Abstract
In their work on human cancer, the International Agency for Research on Cancer have run a programme of «monographs» that evaluate carcinogenic risk of chemicals to man. The data collected provide considerable information on the risk from substances identified as carcinogens. However, this is largely unused in the IARC classification scheme in spite of the use of the term `risk' in the title and text of the monographs. Consequently, some governments and pressure groups use hazard identification to advance the cause for banning agents without conducting a risk assessment. Confusion and indiscriminate use of `hazard' and `risk' mean that the hazard data are commonly misrepresented as risk data. A common political response is to push regulatory action to extremes, citing the Precautionary Principle. Unfortunately, eliminating substances on the grounds of inherent hazard can deny major benefits to societies and undermine the sustainable developments. This is nowhere better illustrated than in the case of the minerals known collectively as asbestos. Evidence available clearly differentiates the hazards of chrysotile and amphibole asbestos, yet the current IARC classification does not make this distinction. This is in spite of the fact that amphibole asbestos produces orders of magnitude more diseases than chrysotile when used in the same way. The overwhelming weight of evidence available indicates that chrysotile can be used safely with low risk. Cement products such as water pipes and boards for housing provide are versatile products made at affordable cost for the developing countries which if not available would cost rather than save lives.
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 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 | 0.200 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.013 | 0.050 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".