To Invest or Sell? The Impacts of Ontario’s Greenbelt on Farm Exit and Investment Decisions
Bibliographic record
Abstract
Abstract This article examines the impact that Ontario’s Greenbelt legislation, a farmland preservation policy implemented in 2005 that permanently protects over 1.8 million acres of land from non‐agricultural development, has on farmers’ exit and investment decisions. There are conflicting hypotheses regarding the impacts that farmland preservation could have on farmers’ management decisions with respect to investment or disinvestment, and there is a lack of evidence in the literature regarding the nature of such impacts. To address this issue, this article uses a farm‐level panel data set to estimate the impacts of the Greenbelt policy on farm exit and on farm investment. The Greenbelt policy is found to have influenced both farm exit and farm investment decisions, with the impact varying depending on location within the Greenbelt. In particular, the results indicate evidence of a negative impact on farm investment, which is contrary to one of the objectives of the Greenbelt policy.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".