Bibliographic record
Abstract
The current issue of Tobacco Control highlights recent developments in research related to tobacco packaging, including issues as diverse as health warnings, plain packaging, messaging and novel products. Since the implementation of the first pictorial cigarette warnings by Canada nearly two decades ago, to the first implementation of plain packaging (PP) by Australia in 2012, the literature on this field has blossomed and diversified. The ability of health warnings to remain relevant and continue to command attention is an important question in public health education. Woelbert and d’Hombres1 conducted a large experimental study in 10 European countries that had implemented differing warning styles. They find that graphic and text combined have a maximum effect in reducing intentions to smoke, but that this was attenuated in countries that had already implemented this warning style. Novel pictures reversed the wearout effect, providing evidence to support rotating warning content over time. Green2 and colleague address the issue of changing warnings from a different perspective, looking at the introduction of new warning content and the removal of other content as new, larger warnings were implemented in Canada. Adding and removing content from warnings generally affects smoker knowledge—removing carbon monoxide (CO) and impotence warnings led to decreased awareness that these were associated with smoking while adding blindness and bladder cancer warnings increased awareness, although adding an addiction warning had a little effect. Novelty appears to be important to consider when adding content, though it is also important to consider the …
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".