Bibliographic record
Abstract
The food and tobacco sectors commonly make use of the same product descriptors, including claims relating to health. In their Tobacco Control paper, Raskind and colleagues make a valuable contribution by highlighting Winston advertising that makes use of claims relating to ‘plant-based’ menthol cigarettes.1 The authors call further attention to Winston’s advertising as an example of health-related claims being made where familiar terminology in the food sector is also applied to cigarettes. Plant-based products are commonly positioned as alternatives from red meat or dairy consumption based on claims pertaining to health and environmental sustainability (figure 1A).2–4 Figure 1 (A) This print advertisement for Silk plant-based products circulated in the March/April 2020 issue of Canada Convenience Store News . The ad copy states, ‘Plant-based goodness. Whenever. Wherever. Good for you!’ The statement attributed to the asterisk is ‘As part of a balanced diet and a healthy lifestyle.’ (B) Advertising for Kraft Light peanut butter indicates: ‘For health nuts. With 25% less fat, who needs to work out?’ Kraft was previously parent-owned by …
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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".