Bibliographic record
Abstract
Export "A" ads are extremely expert, eh?The RJ Reynolds-Macdonald advertising campaign for Export "A" in Canada features solo athletes from various extreme sporting events that they sponsor with the slogan "Go your own way" (fig 1).These ads contain almost all of the elements that its competitors, its US parent, RJ Reynolds, and other transnational firms have found to be so vital and necessary to the commercial success of cigarette brands and industry.Despite the fact that these are sponsorship ads, rather than direct merchandising ads which display the product, they none-the-less dramatically promote brands which are well known to Canadians as cigarettes, and only cigarettes.The extreme importance of starters Cigarette brands enjoy a phenomenally high rate of brand loyalty, consistent with their addictiveness.This brand loyalty is higher than that seen with any other consumer goods.Annual brand switching rates are very low.Those that do switch are not only few in number, but often the most anxious about health risks, and also demonstrably fickle.These traits make switchers a most unattractive type of customer.In contrast, because of the high brand loyalty (year to year retention of customers), starters are highly attractive, as capturing starters builds a solid franchise base that is enjoyed for years.The firms that succeed in capturing starters come to dominate the industry, as best evidenced by Marlboro's history.Put most simply: "To
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.465 | 0.148 |
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".