Planning for public parks to meet urban forest objectives : a Canadian perspective
Bibliographic record
Abstract
Municipal parks and their services have evolved during the past decades, from pastoral grounds to sport and recreation assets, urban agriculture hubs, and venues for cultural events. Public municipal parks can also play a significant role in achieving municipalities’ strategic goals and canopy targets. With a changing climate and biodiversity loss occurring around the world and particularly in urban areas, enhanced tree planting in parks could help meet demands for ecosystem services. However, various challenges are associated with these efforts. This study investigates park policy, planning, and design in Canadian municipalities and their incorporation of urban forestry objectives. The research includes in-depth, detailed examination of municipal public parks (case studies); park-user interviews and surveys; and interviews and surveys of professionals involved in park planning, design, and development in Canadian cities. Professional interviewees include municipal staff, landscape architects, and park planning consultants. Results show a lack of clear direction and coordination between urban forestry objectives and municipal park planning and design. The research identifies a need for more clear alignment between urban forestry policy and recommendations and park planning and design practices in selected Canadian municipal parks. While municipalities are working towards achieving sports, recreation, and cultural needs, restrained policy and design practices within Canadian municipal parks can negatively affect the fulfillment of municipal canopy targets and urban forestry programs in general. This study provides recommendations on how municipalities can manage their urban parks to better balance a range of objectives related to recreation, arts, and culture on the one hand, and environmental services and other urban forest objectives on the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".