Editorial – Is health promotion in Canada, or anywhere else, different from Australia?
Bibliographic record
Abstract
Last month, I was at a health promotion conference in Europe and overheard a remark at dinner. The conversation seemed to be about how other countries ‘do’ health promotion. Pricking up my ears at the word ‘Australia’, the remark was not about the quality of our work, but our attitude towards it. I cannot get the word they used right here, but the phrase that comes to my mind in trying to capture the sentiment is one we used to hear a lot last century: ‘cultural cringe’. Put more bluntly, according to my dinner companions, Australia (and a couple of other countries, I gather) don't seem to recognise that they are first class when it comes to health promotion. They think that the rest of the world must be ahead of them. Funny that. Yes, it's silly. And then the conversation moved to something else.
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 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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.029 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".