Capacity of Ontario municipal planning departments to support local and regional agri- food systems
Bibliographic record
Abstract
Municipalities play an important role in supporting and facilitating agri-food system growth that is economically sound, environmentally sustainable, and aligned with provincial priorities. Municipal planning departments have the ability to enhance or hinder agri-food systems through the creation, implementation, and enforcement of policies, programs, and initiatives related to agriculture and agri-food systems. Research carried out in 2020- 2021 revealed that municipal capacity in Ontario’s Greenbelt region is varied in terms of staffing, budget, knowledge, and department structure, among other factors. The impacts of municipal capacity affect whether municipalities are able to proactively or reactively plan for agri-food systems. This project looks to extend previous research based in the Greenbelt to the rest of the province. The Greenbelt region of Ontario is unique and is not representative of municipal experiences in less urbanized regions. Assessing municipal capacity to support agri-food systems is important to understanding the ways varied municipal capacity impacts the agri- food sector (programs, policies, plans, etc.), helping to target provincial government support, and allowing for knowledge sharing between municipalities. This project will include both Southern and Northern Ontario and aims to capture the range of agri-food related challenges facing regional governance across the province. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".