Strategic planning in urban forestry: A 21st century paradigm shift for small town Canada
Bibliographic record
Abstract
The pressures created by urban sprawl are leading to a reduction in forested land in Canada and North America. Poorly controlled land-use planning contributes to the haphazard urbanization of many small communities within commuting distance of major urban centres. Urban forests are largely ignored as an asset and the potential benefits they can offer to communities are often not acknowledged in the planning process. Relatively few communities across Canada have any form of urban forest management. A new definition of the urban forest is proposed that recognizes the need for an ecosystem approach to urban forest management and the integral role that humans play in that ecosystem. To facilitate the implementation of urban forest management plans in small communities, a simple strategic planning framework is presented. Using this approach, many small towns can maintain their rural character and benefit from a wealth of environmental, social and economic benefits. Key words: urban forestry, community planning, forest benefits, strategic planning, ecosystem approach, forest fragmentation
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".