The Development of a Provincial Food and Nutrition Strategy through Cross-Sector Collaboration
Bibliographic record
Abstract
A whole-system perspective is critical in efforts to create a healthy population and a productive, equitable, and sustainable food system. In 2009, the Ontario Collaborative Group on Healthy Eating and Physical Activity undertook a bold initiative to develop a comprehensive provincial strategy encompassing the entire food system. The Ontario Food and Nutrition Strategy was shaped through extensive consultation with diverse stakeholders. This strategy identified strategic directions and priority actions for productive, equitable, and sustainable food systems intended to promote the health and well-being of all Ontarians. Paramount to the strategy is a collaborative governance mechanism allowing for a cross-government, multistakeholder coordinated approach to food policy development. Key actors participated in a collective impact process to develop a theory of change and potential governance model. Different models for collaborative work were examined and a governance model for a multistakeholder coordinated provincial mechanism was proposed. Lessons learned from this process will inform others involved in food systems work at the provincial, regional, or local level and may pave the way towards successful inter-sectoral action on priority recommendations geared towards improved nutrition-related and food systems outcomes.
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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".