Death and taxes: The framing of the causes and policy responses to the illicit tobacco trade in Canadian newspapers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it