The Impacts of On-Farm Diversification to the Family Farm and the Intersection with Land Preservation and Public Planning Policy in Ontario
Bibliographic record
Abstract
Family farming has progressed and evolved over time, which has included changes in gender roles and the introduction of pluriactivity on many farms. The history of farming leads to the discussion of family farm entrepreneurs and on-farm diversification. Ontario is now home to a variety of on-farm diversification initiatives, including roadside fruit and vegetable stands; sugar bush educational experiences; various wineries, breweries and distilleries; small scale restaurants; wedding venues; concert venues; horse riding ranches; eco-adventure facilities, including zip lines and tree-top canopy adventures, children’s adventure farms and seasonal venues; dirt bike courses; overnight accommodations and much more. \nThe land use planning regime applied in rural Ontario is primarily based on agriculture and farmland preservation. In 2016 the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) created the Guidelines on Permitted Uses in Ontario’s Prime Agricultural Areas (Guidelines), the first planning tool to assist local municipalities in creating opportunities for on-farm diversification. This dissertation examines the history and evolution of family farms including current day on-farm diversification trends. This dissertation describes the current planning policy regime in Ontario that allows for on-farm diversification opportunities and requirements for the preservation of agricultural lands by examining the Provincial Policy Statement and outlining the purpose and effect of Official Plans, Zoning By-laws and municipal land use processes. Then, independent research through surveys, interviews and focus groups with Ontario farmers and Professional Planners is summarized and analyzed to provide recommendations on balancing on-farm diversification and agricultural preservation. It is discovered that the OMAFRA Guidelines are an excellent and useful tool in creating on-farm diversification opportunities while also meeting Ontario’s legislation to protect prime agricultural areas. However, the OMAFRA Guidelines are significantly underutilized, not interpreted or implemented consistently across Ontario and require updating and the possible creation of a user tool to assist Ontario farmers and Professional Planners in creating responsible and resilient new land uses on family farms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".