Innovations in Canada That May Inform the Next EU Tobacco Products Directive
Bibliographic record
Abstract
Introduction This presentation will outline, with images, the successful Canadian experience with a series of innovative regulatory measures, often world firsts. These innovative measures and this experience could be considered for the next EU Directive. Material and Methods The presentation will draw on Canadian legislation, regulations, research, and advocacy. Results The measures adopted in Canada include the following: Health Warnings • Require health warnings directly on every cigarette (announced June 2022, final regulations pending) • Require world’s largest package health warnings for surface area (in cm2), due to minimum dimensions for “slide and shell” packaging • Require health messages inside packages on slide of “slide and shell” packages, replacing inserts Regulation of the product: flavours, dimensions, filters • Ban menthol/cloves at ingredient level in all tobacco products • Ban characterizing flavours in all tobacco products • Ban superslims, slims and wide cigarettes, with diameter minimum 7.65 mm and maximum 8.0 mm • Ban cigarettes longer than 85 mm • Require flat end to filter thus banning recessed filters • Require little cigar diameter of minimum 7.0 mm, maximum 8.5 mm Plain packaging • Require plain packaging for all tobacco products • Require standardized “slide and shell” format, resulting in (1) increased warning size; (2) a more inconvenient package size; (3) interior health messages that are not discarded • Ban brand names that evoke a colour or filter characteristic • Require brand name (including brand variation) to appear on a single line • Require drab brown inside pack • Require little cigar diameter minimum 7.0 mm, maximum 8.5 mm Conclusions Canada has successfully implemented a series of innovative regulatory measures, thus demonstrating feasibility for potential inclusion in the next EU Directive.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".