Bibliographic record
Abstract
Canada’s parliamentarians pass Bill C-260, an amendment to the Hazardous Products Act creating a reduced ignition propensity cigarette It was not without some sense of historical irony that Liberal Member of Parliament in Canada, The Honourable John McKay, observed, while speaking in favor of a proposed legislative amendment, that in February 1916 much of Parliament burned to the ground. Although no official cause was ever provided, it was widely believed that a cigarette caused this fire.1 Andrew McGuire performs a great service in this issue of capturing the process that has led, after 30 years of struggle, to the introduction of safer cigarettes [see page 264] . The tactics he and his colleagues used are masterly and conjure up an image of a David and Goliath-like struggle to change the tobacco industry’s practises. In addition to admiring and acknowledging the skills displayed by McGuire, I confess to being one of his “thousands of advocates” who have also engaged the tobacco industry on this subject. For a decade, spanning the mid 1980s to mid 1990s, in conjunction with local champions in the fire suppression services, I worked on a number of fronts. Most memorable was the one involving the Product Safety …
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".