Rapidly increasing promotional expenditures for e-cigarettes
Bibliographic record
Abstract
Awareness and use of e-cigarettes have increased rapidly, and the products now represent a billion-dollar industry in the USA.1 ,2 Public health concerns about e-cigarettes centre on their potential appeal to the youth market,3 limited scientific evidence regarding their impact on individual and population health,4 and inconsistent product standards, including variations in nicotine content within and across brands.5–7 While some US cities have extended public smoking bans to cover e-cigarettes or taken other restrictive measures,8 ,9 the products remain unregulated at the federal level. Globally, there is significant variation in how products are treated, with some countries including Canada and Australia taking a more restrictive approach.10 A recent proliferation of e-cigarette marketing—including ads in media where traditional tobacco advertising has long been prohibited, such as television1—likely plays a key role in the exponential growth of the products’ popularity. With some exceptions,11 ,12 reports of e-cigarette marketing to-date have been mainly anecdotal; surveillance and tracking of the quantity and content of such marketing is much needed. The goal of this Industry …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".