Expansión de la industria tabacalera y contrabando: retos para la salud pública en los países en desarrollo
Bibliographic record
Abstract
The international tobacco industry, in its constant quest for new markets, has expanded aggressively to middle- and low-income nations. At the same time there has been a marked increase in tobacco smuggling, especially of cigarettes. Smuggling produces serious fiscal losses to governments the world over, erodes tobacco control policies and is an incentive to international organized crime. In addition, smuggling results in increased demand for and consumption of tobacco, which in turn benefits the tobacco companies. Moreover, there is evidence indicating that the international tobacco industry has instigated cigarette smuggling and has participated directly in these activities, while at the same time carrying out costly lobbying campaigns to pressure governments against tax increases and to promote their own interests. Academic studies and empirical evidence show that tobacco control can be promoted through high tax rates without causing significant increases in smuggling. To achieve this tobacco smuggling must be attacked through the use of strategies including multilateral controls and actions such as those included in the Framework Convention on Tobacco Control, which establishes the basis for combating smuggling through an international, global approach. It is also necessary to increase the penalties for smuggling and to make the tobacco industry, including producers and distributors, responsible for the final destination of their exports.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".