Fighting Tobacco Smoking - a Difficult but Not Impossible Battle
Bibliographic record
Abstract
According to the World Health Organization (WHO), tobacco-related disease is the single largest preventable cause of death in the world today, killing around 5.4 million people a year--an average of one person every six seconds. The total number of death caused by tobacco consumption is higher than that of tuberculosis, HIV/AIDS and malaria combined. Unlike other communicable diseases, however, tobacco-related disease has a man-made consensus vector--the tobacco companies that play an active role to promote tobacco consumption, which directly heightens the disease morbidity. Any public health policy designed to curb smoking behavior has to prepare for opposite lobbying actions from tobacco companies that undermine the effects of the health measures. Another unique nature of the tobacco epidemic is that it can be cured, not by medicines or vaccines, but on the concerted actions of government and civil society. Many countries with a history of tobacco control measures indeed experienced a reduction of tobacco consumption. As most of these governments launched a range of measures simultaneously, it is hard to quantify the relative merits of different control strategies that contributed to the drop in the number of smokers. These packages of strategies can come in different forms but with some common features. Political actions with government support, funding, and protection are crucial. Without these, antismoking efforts in any part of the world are unlikely to be successful.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".