Sowing support: the importance of land use policy in planning for small-scale agriculture in Ontario
Bibliographic record
Abstract
Agriculture is changing in Canada. The average size of farms is increasing while the overall number of farms nationwide continues to fall, with the trends towards farm consolidation and industrialization putting smaller farms at risk of disappearing forever. Nevertheless, Ontario’s small-scale farms continue to be an important facet of rural communities, with many positive social, economic, and environmental impacts. Planning in general, and land use policy specifically, has a major role to play in protecting farmland and ensuring long-term viability. This paper seeks to understand the effects of land use policies on the viability of small-scale farms in Ontario’s Greater Golden Horseshoe through the study of three upper-tier municipalities and their constituent lower-tier municipalities. By examining the challenges faced by farmers, as well as how these are addressed, we can begin to understand where the blindspots are and what rural municipalities can do to better support small-scale agriculture. Key Words Agriculture, small-scale, Ontario, land use policy, farmland, rural 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".