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Expansión de la industria tabacalera y contrabando: retos para la salud pública en los países en desarrollo

2006· review· es· W2154129557 on OpenAlexaff
Pedro Enrique Armendares, Luz Myriam Reynales-Shigematsu

Bibliographic record

VenueSalud Pública de México · 2006
Typereview
Languagees
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsTobacco controlTobacco industryConsumption (sociology)BusinessIncentiveConventionInternational tradePolitical scienceEconomic policyWelfare economicsEconomicsPublic healthMedicineMarket economyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.351
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2006
Admission routes1
Has abstractyes

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