Bibliographic record
Abstract
This article presents Canada’s major social marketing achievements and contributions to date, the strengths of the Canadian social marketing field, and the challenges it currently faces. As an early adopter of social marketing, Canada has been integrating this unique form of marketing into its public health and environmental strategies for over 40 years. The Canadian track record includes successful initiatives, major events, seminal publications, high-quality training programs, as well as academic and professional centers that have had an impact in Canada and around the world. The field is currently facing some challenges, however. If this remarkable story is to continue, Canadian social marketing leaders will need to rally around a number of collective initiatives to advance the field, promote its value, mobilize resources, and attract a renewed network of practitioners and academics.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".