Legislating for a Tobacco Free Society Conference Proceedings: hosted by Michael Martin, Minister for Health and Children. 31st October to 2nd November, 2001, Dublin Castle Conference Centre, Dublin.
Bibliographic record
Abstract
Across the world there is a growing awareness of the enormous problems associated with \ntobacco addiction and the need for concerted responses from legislators. In the first event of \nits kind in Ireland international experts, politicians and policy makers came together to share \ntheir experience of initiatives aimed at tackling the tobacco epidemic. \nThe conference featured presentations by those directly involved in developing and \nadministering a legislative response to this pandemic. Eminent speakers from the USA, \nAustralia, Canada, Finland, Poland, Scotland, UK and Ireland dealt with the many facets \nof tobacco control and tobacco use in different parts of the world as well as at global and \nEuropean levels. \nSmoking not only endangers the health of smokers themselves, exposure to environmental \ntobacco smoke (ETS) or passive smoking is a significant threat to the health of others. \nSpeakers with experience in developing successful prevention strategies discussed the \ndangers of ETS and demonstrated the benefits of protective measures.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.331 | 0.096 |
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".