Municipal Agri-Food Systems Planning Capacity - Lessons Learned from Across Ontario
Bibliographic record
Abstract
Municipal governments in Ontario play a key role in agri-food systems planning. Through land-use planning, economic development, and broader decision making, municipalities have the ability to encourage or hinder agri-food systems in their communities. While the provincial government provides a policy framework, local governments have the ability to determine how best to implement policies in their jurisdiction and have the ability to go beyond provincial mandates. This flexibility allows local governments to pursue policies, programs, and plans to support and respond to the agri-food sector. However, little is known about municipal government capacity to pursue agri-food systems planning. This presentation will discuss the findings of a research project that looks at the capacity of municipalities in Ontario related to agri-food systems planning. More specifically this research addresses the following questions: How can municipal agri-food systems planning capacity be conceptualized? What factors contribute to municipal agri-food systems planning capacity? What opportunities are available to help municipal planning departments build capacity in supporting sustainable and resilient local and regional agri-food systems? This presentation will share key findings and insight from this research, including best practices for supporting and responding to local and regional agri-food systems. This research confirms that municipal capacity to support agri-food systems planning is variable. Factors contributing to municipal capacity include: department resources and characteristics, relationships with other municipal departments, relationships with external actors, and commitment to local and regional agri-food systems. This capacity positions municipalities to facilitate agri-food systems planning processes including the use of regulatory and non-regulatory tools, and leveraging partnerships in support of agri-food systems planning.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".