Planning Green and Public Spaces in Lawrence Heights, Toronto: Considerations for \nMeaningful Community Engagement
Bibliographic record
Abstract
The vitality of many green and public spaces is seemingly lackluster in appearance and deficient in spatial and social programming. Urban planning and design are influential of the spaces that communities make meaning in and respectively animate. Approaching planning through a community development perspective embraces community assets and the organizational capacity through which communities can actively participate in the making of their public realm. By deviating from conventional planning practices, planners can more thoughtfully address social and environmental injustices—acknowledging, respecting, and embracing diversity and difference through green and public space planning and design. \nThis research adopts a qualitative mixed-methods approach to understand how communities are involved in the shaping of their green and public spaces as well as the mechanisms in place that support this process. Primary and secondary data were collected through two knowledge exchanges, four semistructured phone interviews, a walk-along with residents, personal site observations as well as a comprehensive review of pertinent policy frameworks, planning documents, media, and scholarly literature. Research findings revealed that people strive to make meaning in spaces. A socio-spatial study of Lawrence Heights demonstrates that green and public spaces foster social relations, human health and well-being, and are often at the intersection of community engagement and development (or lack thereof).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".