Bibliographic record
Abstract
Drs. Lee and Chen provide data on the effects of price and smoking characteristics and their relationship to smuggled cigarettes in Taiwan.Smuggled cigarette smoking increased for each NT$1 increase in the price of legal cigarettes.Globally, cigarette smuggling as a percent of consumption varies widely.In Israel, it is estimated that 44% of the cigarettes are smuggled (1999 WHO).In Hungary, the rate is 9% and in Egypt it is slightly above 1%.Researchers estimate that 30% of the internationally exported cigarettes (about 355 billion cigarettes) are lost to smuggling.This trend is seen globally, both in the developed and developing world.Cigarette taxes are often used to plug holes in budgets at all levels of government.Here in my home state of New Jersey, for instance, the newly proposed tax for July 2006 on a pack of cigarettes is $2.75, up from the current $2.40 USD per pack.Initially, state and federal governments benefited from the increased revenue, and we saw the desired decrease in cigarette consumption.But over time, sales of legal cigarettes decreased and illegal cigarettes were smuggled into the higher tax states for resale.A report from Tobacco Free Kids estimates that approximately one-quarter of all legally exported cigarettes end up smuggled across international borders.The World Bank ( Joossens, 1999; De Beyer, 2002) has identified countries with high and low smuggling rates.Sweden, Denmark, Norway, France, Finland, and Ireland-all with high cigarette prices and taxes-are reported to have low smuggling rates (,5%).Countries with low cigarette prices and taxes are reported to have higher smuggling rates (.10%): Spain, Italy, Pakistan, Nigeria, Yugoslavia, Ukraine, Moldova, Columbia, Iran, Austria, and Cambodia.Similar to the recommendations made by other researchers, the authors sanction better controls, forgery-proof tobacco tax stamps, and other regulatory systems.The task will be difficult given that we estimate overall smoking prevalence is still about 29% globally, with more than 82% of smokers belonging to low- and middle-income groups.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.515 | 0.458 |
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".