The integration of the food system into the land-use planning framework in southern Ontario
Bibliographic record
Abstract
Food security is integral to the health and well-being of communities. However, regional food systems, which (in part) facilitate food security are unevenly acknowledged in Ontario’s land-use planning framework. The intersection of regional food systems, provincial land-use planning and policy development processes is well known. However, the primary focus of this relationship by decision-makers is generally the financial outcomes of the agricultural sector and the food system, and how to leverage the planning framework to maximize profits, for the benefit of economic development. As a result, public health considerations, environmental values and the right to nutritious and sustainable food may be less prioritized, rendering gaps in the way we manage this critical resource. This study seeks to understand how Ontario’s current land-use planning framework can be adapted, to expand inclusion and consideration of the nonmarket values of regional food systems to achieve comprehensive regional food security in Ontario. Using a foodshed approach, I developed a rubric to evaluate land-use planning frameworks across Canada and two American jurisdictions to better understand the state of food systems in relation to planning. I conducted a Jurisdictional Scan to report my findings and conducted interviews with subject matter experts to understand the current interactions between the food system and planning in Ontario. The results of my research study indicate that while food systems are almost completely absent from Ontario’s planning framework, there are many lessons learned and best practices that can be integrated in Ontario. Furthermore, there is an infrastructure in place that can be expanded through amendments to existing legislative, regulatory and policy infrastructure to include more comprehensively food systems in the planning system.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".