Looking for the next breakthrough in tobacco control and health
Bibliographic record
Abstract
South African statistics on cigarette smoking suggest that there are grounds for some celebration on how rapidly consumption has fallen since the institution of anti-smoking policies started roughly 20 years ago. As with other countries, tax policies that increased the cost of cigarettes will have played the greatest role in the reduction in smoking. These policies have also resulted in strong economic benefits, saving up to 1.5 million lives and put over US$12.5 billion into the economy. The authors of this paper were both very involved in achieving these policies: in 1993, Derek Yach hosted the first national meeting to brief the African National Congress on the need for stronger taxes to address tobacco use, and in that meeting David Sweanor outlined the success taxes had already achieved in his home country of Canada.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".