The need for contextual, place-based food policies: Lessons from Northwestern Ontario
Bibliographic record
Abstract
In recent years, several reports have highlighted the need for a national food policy that takes a comprehensive approach to addressing food systems (CAC, 2014; Levkoe & Sheedy, 2017; Martorell, 2017; UNGA, 2012). These findings suggest that, at the core, resilient food systems must be built on interconnected knowledge and experience that emerge from place-based interrelationships between human and ecological systems. Drawing on these important learnings, this commentary voices our hopes and concerns around the recent efforts of the Canadian Government to develop a food policy for Canada. While we commend the Government’s desire to “set a long-term vision for the health, environmental, social, and economic goals related to food, while identifying actions we can take in the short-term”, we caution any tendency to develop “best practices” that assume a universal, or “one-size fits all” approach to food policy development. We argue that Canada requires a set of contextual, place-based food policies that emerge from the grassroots, address local needs and desires, and build on the strengths and assets of communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".