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E-cigarette use in public places: striking the right balance

2016· article· en· W2554103701 on OpenAlexaboutno aff
Linda Bauld, Ann McNeill, Peter Hájek, John Britton, Martin Dockrell

Bibliographic record

VenueTobacco Control · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersEconomic and Social Research CouncilMedical Research CouncilCancer Research UK
KeywordsTobacco controlPublic healthBusinessPopulationAdvertisingBalance (ability)Tobacco industryPolitical scienceEnvironmental healthLawMedicine

Abstract

fetched live from OpenAlex

First paragraph: The availability, marketing and use of electronic cigarettes or nicotine vapourisers is subject to a range of regulations across different countries, varying from the prohibition of sales and use, to little or no regulation. The most common approach adopted in countries that permit some use of e-cigarettes has been to adapt existing laws or frameworks designed for tobacco products to include e-cigarettes. This can include extending bans on tobacco advertising to e-cigarettes, applying the same age of sale laws or taxing e-cigarettes like tobacco. In some jurisdictions including many Canadian provinces and US states, existing smoke-free public places laws have also been amended to include e-cigarettes, so that vaping is prohibited wherever tobacco cannot be used—including enclosed public places, workplaces and some outdoor areas. But is this the right approach, and is it supported by research evidence?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2016
Admission routes1
Has abstractyes

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