LOCAL AGRI-FOOD SYSTEMS AS A MUNICIPAL PRIORITY: CONSIDERING THE ROLE, APPROACH AND CAPACITY OF MUNICIPAL PLANNING DEPARTMENTS
Bibliographic record
Abstract
In a context of increasing volatility and instability, agriculture and local agri-food systems are critical to local and regional resilience.In Canada, agriculture is both a federal and provincial responsibility, however municipalities are the most local level of government responsible for land use decisions.While some guidance is provided by the provincial government, municipal planning departments play a critical role in creating, implementing, and enforcing policies, programs, and initiatives related to agriculture and agri-food systems.Municipalities are also responsible for implementing provincial guidance and directives, and are key players in ensuring consistency and farm viability across the province.However, little is known about the capacity of municipal planning departments and their role and approach to supporting agriculture.This paper looks to examine the role and approach of municipal planning departments in agri-food systems in Ontario.Academic literature on the topic was coded for 10 possible roles adopted from earlier publications looking at the intersection of planning and food systems.Findings are interwoven with interview and survey data regarding the capacity of municipal planning departments in Ontario's Greenbelt region to support agri-food systems.While this paper provides insight into the role of municipal planning departments in agri-food systems in Ontario, further research should investigate the capacity of municipal planning departments to carry out these roles.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".