“Australia is one of the darkest markets in the world”: the global importance of Australian tobacco control: Figure 1
Bibliographic record
Abstract
"Australia is one of the darkest markets in the world... it probably is the darkest, I mean ourselves and Canada fight every month for who's got the darkest conditions to do tobacco manufacturing and marketing. And one of the things we can offer the world is what we do best, which is how to work, maximize, proactively drive our market position in a market that's completely dark. Now that takes a different skillset... a different type of learning. We need to export that... we know we have a lot of expatriates who come down to Australia for learning. they can come here and learn these techniques and take them back to Europe or Latin America or to the United States or to Africa... But the other thing that is really good for us is that we are also a huge net exporter of Australian talent. about 30 or 40 people currently off-shore... We do things really differently here than most other BAT organizations." David Crowe, Marketing Director, British American Tobacco (BAT) Australia(1).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".