Retirement lot severances in Ontario: an assessment of ownership patterns, location, and perceptions
Bibliographic record
Abstract
The continual depletion of farmland is an increasing concern for reasons that range from food security to supporting local economies. Within the Province of Ontario the concern is enhanced with growing urban pressures and the fact that 52% of Canada's Class 1 agriculture land is located in Ontario. With the patterns of development growth on the rise in rural areas across Ontario, the responsibility of changing and updating planning policies that address agricultural land use needs to be acknowledged. This research examined retirement lot severances, one of the ways in which agricultural land is currently being depleted in Ontario. Among the three case studies completed, the data revealed that within five years of when a retirement lot severance was granted nearly 50% of these lots had been transferred from the original applicant. Additionally, the majority of these severances were located on prime agricultural land. From the data found, it is recommended that policies concerning retirement lot severances be re-evaluated to no longer permit these severances, especially on prime agricultural land.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".