Lessons From Developing Food Policy Through A Multistakeholder Governance Network: A Case Study Of The Canadian Food Strategy
Bibliographic record
Abstract
Globally, we are facing a food system in crisis. Now more than ever, food policies are crucial to the future of food. In Canada, there has never been a national food policy that looked at the food sector holistically. It has traditionally centred on food safety and agriculture, sidestepping many other vital issues. However, between 2010 and 2014, four non-state actors developed national food policy documents. In response to these developments, this study asks: What are some unique characteristics of multistakeholder networks in the policymaking process? To answer this question, the Canadian Food Strategy (CFS) created in 2014 by the Conference Board of Canada (CBoC) was used as a case study. This strategy was unique because it involved a range of food policy issues, food policy actors, and had financial support from several key food industry players. Participants in this policy development experiment did not deem this strategy a success regardless of its abundant financial resources, its topic comprehensiveness, and widespread buy-in from food industry, government, and other non-governmental organizations. Semi-structured and elite interviews were used to shed light on why this case was not successful, to extract lessons from this initiative for future food policymaking efforts in Canada. This dissertation integrated wicked policy, governance, policy network, and multistakeholder literature to understand how food policy may be developed and governed in Canada. The study resulted in three key findings. First, food policy in Canada is very complex, exhibiting both tame and wicked qualities. Second, the state must have a significant position in a multistakeholder food governance network (MFGN). Lastly, while the structure of the MFGN and the actors involved in it are important to a network’s successful policy outcome, the CFS initiative revealed that process was fundamental to the outcome.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".