Bibliographic record
Abstract
On the same day that Formula One (F1) strategies to undermine tobacco control legislation were discussed at the World Conference on Tobacco or Health in Helsinki, news agencies reported that the Canadian Grand Prix was to be dropped from the 2004 calendar. F1 boss Bernie Ecclestone insisted that tobacco advertising was the sole reason for the decision. “Our problem is quite simple. The Formula One teams with tobacco-related sponsorship lose part of their revenue when a certain percentage of the events ban tobacco sponsorship.” This was the reason the Belgian Grand Prix was not included in the 2003 calendar, he added. ![Graphic][1] Canada: A bilingual postcard created by Carte Blanche, a communication marketing agency in Montreal, pre-addressed to Bernie Ecclestone for Canadians to send to the Formula One boss to protest against the loss of Canada’s top motor race. In all, 108 000 cards were distributed by the Pop Media network, and several newspapers ran ads to build awareness and support for the campaign. On the back of the card was a message asking Ecclestone whether F1 was as addicted to the tobacco industry as are billions to cigarettes, and demanding that he reconsider his sponsorship policy. In Belgium a law was passed in 1997 banning all tobacco advertising and sponsorship from 1 January 1999 (including F1 sponsorship). Since January 1997, there have been five attempts in the Belgian … [1]: /embed/graphic-1.gif
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.088 | 0.028 |
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".