PII: S0003-4878(97)00018-5 LETTER TO THE EDITOR 'MAGIC, MENACE, MYTH AND MALICE'
Bibliographic record
Abstract
If one were looking for a balanced account, the 'horror, shock, probe ' tone of the title of Professor Liddell's editorial strikes a note of ill omen that is echoed in the repeated outbursts in the text. By no stretch of the imagination can it be claimed that it represents an even handed, well researched, accurate account of the history involved. In his review, Liddell distorts the chrysotile controversy by omitting to acknowledge in full the part played by industry in a long and sorry operation that delayed the amelioration of conditions for exposed workers. To be fair, he does instance one episode when the German Asbestos industry misused McGill data, which he categorised as mischievous. (To the simple soul, heedless of the laws of defamation, some word more severe than mischief would suggest itself in this instance.) If Liddell had studied the archives now freely available, he would have been able to acquaint the reader with the extent of even naughtier behaviour by industry. The late Irving SelikofF and his putative Lobby are caricatured as the Black Knights, unworthily motivated and funded by some unidentified opposed vested
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.909 | 0.938 |
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".