Resident Knowledge and Support for Private Tree By-Laws in the Greater Toronto Area
Bibliographic record
Abstract
Urban municipalities across North America are developing policies to protect and manage not only public trees but also the numerous trees located on private property. One approach is the creation of private tree by-laws or ordinances that regulate tree removal on all private property through a permitting process. These regulations can successfully protect the private urban forest, particularly larger trees, but their success is dependent on landowners’ willingness to comply given the difficulties of enforcement. This study examines residents’ awareness and support for private tree by-laws in three cities in the Greater Toronto Area (Ontario, Canada) through a written survey that targeted neighborhoods with high tree canopy—places most likely to have trees regulated under the private tree by-laws. Basic awareness about by-laws varied across the five study neighborhoods, and support for specific components of the by-law, including size and number of trees regulated, tree replacement requirements, and permit fees was also mixed. While a larger number of survey respondents felt that their city should not regulate trees on private land than had supported the current by-law, this was still not a majority of responses. Participants with more trees on their property or who had planted trees were significantly more supportive of the regulations, while several socio-demographic characteristics were also significantly related to level of support for the by-laws. The management implications of these results are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".