POWERS: A STUDY IN HEALTH, WORK, AND GENDER
Bibliographic record
Abstract
Asbestos is a naturally-occurring mineral fiber with a silky texture whose distinctive properties-high mechanical strength, even at high temperatures; incombustibility; good insulating qualities; durability; flexibility; indestructibility; and low cost- have caused its widespread use in industry.4 Brazil is among the world's five greatest producers of asbestos and is also an important consumer of the mineral. There is, therefore, a great deal of attention being paid by the world scientific community to Brazilian practices now that most European countries have prohibited the use of asbestos. The largest asbestos mine in Brazil at the present time is located in the Municipality of Minaçu in the State of Goiás. The mine is managed by subsidiaries of the French multinational Saint-Gobain, whose home country has prohibited asbestos since the beginning of 1997. Asbestos is used in thousands of products in Brazil, especially in the construction industry (roof tiles and water tanks) and in other sectors and products such as brakes (linings and pads), joints, gaskets, clutch plates, cloth, and in special coatings and coverings such as paints and floor tiles, among other uses (Giannasi, 1995). Canada (after Russia) is the world's second largest producer of asbestos and is a large exporter of the raw material, but compared to Brazil it uses relatively little asbestos. Annual 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
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".