An Analysis of Urban Forest Policy: A Case Study of Three Mid-Size Cities in Southwestern Ontario
Bibliographic record
Abstract
Urban forestry provides several benefits from improving the quality of life (Sanesi, Colangelo, Lafortezza, Calvo, & Davies, 2017), providing economic benefits (Kim, 2016), and improving the quality of our environment (Dwyer, McPherson, Schroeder, & Rowntree, 1992). Urban forest management plans and other plans and policies are one way municipalities can manage these resources. In Canada only about 20% of municipalities have urban forest management plans and they are missing largely in smaller communities (Bardekjian, Kenney, & Rosen, 2017). Urban forestry is becoming an incredibly important practice as our climate continues to change. Urban forests contribute to making our cities more sustainable while reducing their contributions to climate change (Escobedo, Kroeger, & Wagner, 2011), and reducing climate change’s impact on the municipality (Dwyer, McPherson, Schroeder, & Rowntree, 1992). This research examines urban forest management in Guelph, London, and Waterloo, Ontario by comparing collections of policies related to the urban forest from each municipality with a modified set of urban forest management best practices. The original best practices were modified based on a literature review to ensure that criteria were based on our current understanding of urban forest management best practices. Based on the comparison of the related policies to the modified set of best practices the three cities all show some strong policies in some aspects of urban forest management and are generally headed in the right direction. Some aspects of urban forest management policy are completely lacking throughout the three cities in this study and each city, despite their strengths, have room to improve. Based on this research the trend in Southwestern Ontarian mid size cities appears to be headed in a positive direction where urban forest management is improving, some cities are pushing for more and more progressive policies. Even though there are many positives, urban forest management policies still require improvement before we can expect to see the full benefit of our urban forests while fully minimizing the costs associated with urban forest management.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".