The Case for A National Food Policy Council
Bibliographic record
Abstract
As the Government develops A Food Policy for Canada to provide an integrated approach to food-systems, governance has emerged as a critical issue. This report was compiled at the request of an informal network of organizations from the food business, farming, civil society, philanthropic and academic sectors interested in national food policy, convened by the Arrell Food Institute at the University of Guelph, the Canadian Federation of Agriculture, Food Secure Canada, Maple Leaf Foods, and the McConnell Foundation (see contributors in Appendix I). The report builds on multi-stakeholder discussions that took place in March at the University of Guelph, in June at the Canadian Federation for 1 Agriculture,2 at the Ottawa Food Summit convened by Agriculture and AgriFood Canada, and at a September meeting in Ottawa at which an initial draft of this paper was discussed. The following recommendations are based on food-system governance research from domestic and international jurisdictions. We propose a governance structure that will make adaptive changes to policies, programs and regulations at different levels, over time, and that recognizes the need for a process that goes beyond the initial launch of A Food Policy for Canada. Our recommendations, after analysis and discussion with stakeholders, are: (1) the creation of a National Food Policy Council as soon as possible; (2) implementation of four short-term recommendations for improving food policy governance in Canada; and (3) consideration of certain longer-term options for institutional support of food policy governance.
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 | 0.061 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.025 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.029 | 0.035 |
| Insufficient payload (model declined to judge) | 0.015 | 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".