Death and taxes: The framing of the causes and policy responses to the illicit tobacco trade in Canadian newspapers
Bibliographic record
Abstract
The illicit tobacco trade accounts for 10% of the global cigarette market and results in US$31 billion in lost tax revenues annually. Despite legal prosecution of tobacco companies, and the introduction of new policy responses, the trade has reached an all-time high. Previous research documents how transnational tobacco companies have sought to influence government responses to the illicit trade in various countries through multiple means, including influencing of news media framing. This paper extends this analysis to Canada where the illicit trade is particularly problematic in scale and political complexity. Articles in Canadian newspapers, published from 2010-2015, were systematically searched (n=177) and analyzed to identify dominant frames, frame sponsors and policy positions related to the illicit tobacco trade. The results show that the most common frames present the issue in ways favourable to the industry. The most common non-governmental sponsors of these frames frequently have links to the tobacco industry, which are rarely disclosed. Findings indicate the need for Canadian media to be critical in its use of data sources amid industry efforts to shape public policy, and the importance of reframing policy discussions in public health terms based on independent evidence.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".