“Australia is one of the darkest markets in the world”: the global importance of Australian tobacco control: Figure 1
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
"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).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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