Adoption influences in Ontario’s 50 Million Tree Program
Bibliographic record
Abstract
Ontario’s 50 Million Tree Program (50 MTP) has been underway since 2007, with a mandate to encourage afforestation in the province. Under this program, Forests Ontario provides financial support to offset the costs of planting trees on properties at least one ha in size; in return, landowners agree to maintain their newly planted trees for a minimum of 15 years. The current study examines adoption influences in the 50 MTP, particularly the role of agricultural land rent values (which help to provide an indication of opportunity cost/trade-offs between agriculture versus forests), the per-tree support level offered by the 50 MTP, and personal motivations such as the desire to enhance wildlife habitat. Our results indicate that landowners in census sub-divisions with lower agricultural land rent values (and therefore lower “opportunity costs”) were most likely to participate in the 50 MTP. Further, census sub-divisions with low agricultural rent values were more likely to show increased trends in forest cover. The effect of the per-tree support offered by the 50 MTP (between $1.25-1.35) on participation in the 50 MTP (and on afforestation in general) was explored, but the limited variation in support levels made it challenging to draw definitive conclusions. Finally, a follow-up survey of 50 MTP participants indicated that wildlife and enhancing native forest cover were the most common motivations for participating in the program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".