Cigarette package colour is associated with level of filter ventilation
Bibliographic record
Abstract
The cigarette package is, in many markets, the primary means of marketing and advertising cigarette brands to consumers. While some countries have moved to implement plain packaging, most continue to allow product differentiation on the basis of packaging. Colour is often used to communicate implicit messages about taste, risk and quality, and evidence suggests that package colours are specifically chosen to manipulate consumer perceptions through ‘sensation transference’.1 Filter ventilation also has been shown to influence perceptions of harshness and perceived risk.2 3 Filter ventilation is also closely associated with machine-measured tar yield and is generally higher in brands previously marketed as ‘Light’. Light and similar words have in many cases been supplanted by colours or other descriptive terms.4 Bans appear to have had an effect in reducing false beliefs, but substantial levels of false beliefs persist, most likely due to other cues that remained, including replacement descriptors, colour-coded packs and filter ventilation.5 6 Using a large database of packages, we explored the extent to which package colour is related to filter ventilation. Data for 759 brands purchased …
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".