Power in Policy: Measuring Farmland Loss in Ontario and Testing the Strength of the Greenbelt Act
Bibliographic record
Abstract
Farmland in Ontario continues to be under immense pressure from development associated with population growth and urbanization, such as residential subdivisions, commercial developments, and aggregate operations. Likewise, much of this development consumes large tracts of our most productive prime agricultural soils. This research investigates the strength of existing policy, primarily the 2005 Greenbelt Act, in preserving Southern Ontario’s agriculture by measuring the rate of farmland lost to non- farm land uses from 2000—2017 using official plan amendments. This methodology has been applied across southern Ontario and the results provide an assessment on the effectiveness of the 2005 Greenbelt policy as well as the associated level of food sovereignty available within the province. This presentation will report on the preliminary results of this research project including trends and policy recommendations and will comment on how farmland preservation contributes to the sustainability of rural and small town communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".