The Impacts of On-farm Diversification to the Family Farm and the Intersection of Land Preservation through Public Planning in Ontario
Bibliographic record
Abstract
Have you ever wondered what prompts Ontario family farmers to diversify, what land use planning policies allow for alternative uses on farms and how Ontario can continue to preserve agricultural lands as family farming continues to change? This primary research objective is to understand how on-farmdiversification impacts the family farm and what the intersection of on-farm diversification and land preservation is through public planning policy in Ontario. The 2016 OMAFRA Guidelines on PermittedUses in Ontario’s Prime Agricultural Areas was/is the first tool that provides family farmingentrepreneurs and municipal government planners opportunities to create on-farm diversified uses whilebalancing agricultural land preservation. This research will:
 
 Endeavour to explore which rural municipalities are using this tool efficiently andeffectively; Discover if the Guidelines are assisting entrepreneurs and identifying bestpractices;
 Identify if it is only prime agricultural lands that warrant these Guidelines for landpreservation; Acknowledge if the Guidelines are preserving prime agricultural lands; and
 Propose changes to the Ontario planning policy framework regarding on-farm diversification andland preservation.
 
 Overall, it is simply not enough to preserve agriculture land in Ontario, we must also preserve the family farmer Keywords: on-farm diversification; land preservation; family farm; Ontario planning policy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".