Bibliographic record
Abstract
That asbestos is still being sold despite overwhelming evidence linking it to debilitating and fatal diseases is testament to the effectiveness of a campaign, spear-headed by Canadian interests, to promote a product already banned in many developed countries. Blessed by government and commercial support, asbestos apologists have implemented a long-term coordinated strategy targeting new consumers in Asia, the Far East and Latin America. At industry-backed "conferences" and on government-funded junkets, they spin a web of deceit, telling all who will listen that "chrysotile (white asbestos) can be used safely." The fact that Canada exports over 95% of all the chrysotile it mines suggests that while chrysotile is supposedly safe enough for foreigners, it is not safe enough for Canadians. Asbestos victims in many countries have struggled to gain public recognition of the human cost of asbestos use. In recent years, nongovernmental organizations working with these groups have created a global anti-asbestos virtual network; with the commitment and support of thousands of "virtual members," this network challenges industry's propaganda and exposes the forces that support its cynical attempt to offload this dangerous substance on developing countries.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".