Packaging digital culture to young smokers
Bibliographic record
Abstract
Tobacco companies frequently seek to associate their products with certain lifestyle images in an attempt to foster brand-specific identities for their cigarettes. As demonstrated by industry-sourced segmentation analyses, for example, companies divide smokers into broad groups based on personal characteristics and strategise on how to adapt their products to increase attractiveness for these imagined communities.1–3 Linking tobacco products with the hallmarks of the digital age, such as social media and innovative technologies, seems to have become a marketing goal for a number of tobacco companies.4–6 Indeed, several studies have hypothesised that the use of mobile phones and smoking may be connected (either as complimentary or competing behaviours) in light of the fact that these products may provide users with opportunities for identity formation and rebellion.7–9 Drawing from a larger study of top selling international brands, we report three illustrative case studies which underscore a recognisable pattern of digital culture in package design. Between July 2011 and January 2012, we collected a sample of top market share cigarette brands comprising 193 cigarette packages from 23 countries as part of the wider Chatter Box project hosted at the University of Toronto's Ontario Tobacco Research Unit. The countries targeted were culturally diverse, drawn from five continents (North America, South America, Europe, Asia and Australia). Using a combination of qualitative content analysis and semiotic analysis, we identified a common theme of ‘digital culture’ among a series of packs from countries including South Korea, Japan, Lebanon, Switzerland, Italy, Belgium, Russia, Serbia, the Netherlands and the USA. Kent …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".