Bibliographic record
Abstract
T he researching and writing of this book would not have been possible without the unwavering support of new acquaintances, respected scholars, and old friends.Key contacts in Asbestos were John Millen at the Musée minéralogique d'Asbestos and G. Claude Théroux at the Société d'Histoire d'Asbestos.Both of these men provided invaluable sources and insight into the community, inspiring me to look deeper into the past and question my own assumptions about people and place.The staff at the Hôtel de ville d'Asbestos were also accommodating and helped broaden my understanding of how the community operated in the past and in the present.Beyond Asbestos, the archivists and commissionaires at the Bibliothèque et Archives nationals du Québec made researching in Quebec City fully entertaining, and Jean-Pierre Kesteman directed me to key resources on the early history of Asbestos.Dr. David Egilman first introduced me to the wealth of historical information at Johns-Manville's Asbestos Claims Research Facility and helped bring new context and meaning to this study.Geoffrey Tweedale followed suit, and I appreciated the hospitality he and his wife Mary showed me in Manchester just as much as the sources he provided.Both Egilman and Tweedale took an interest in my study that encouraged me throughout the research and writing process.This book would not be what it is without their generosity.Maggie Baumgardner at the Johns-Manville Asbestos Claims
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.333 | 0.195 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".