The growing role of citizen engagement in urban naturalization: The case of Canada
Bibliographic record
Abstract
The author (MA, MCIP, RPP) co-manages Evergreen's Common Grounds program which focuses on the protection and restoration of public lands in urban areas. He has a Master's degree in urban planning from the University of Waterloo, a Bachelor's Degree in resource geography from the University of Victoria , and he is a full member of the Canadian Institute of Planners. Over the past five years, he has developed urban greening resources for land use professionals and community groups including a national grant program, guidebooks, research reports, municipal policy guidelines and case studies. He has also developed and led professional training workshops for public land managers and other municipal officials on partnership approaches for protecting and stewarding urban green spaces. Prior to joining Evergreen, Stewart worked in the private and public sectors leading a variety of land-use planning, environmental assessment and resource conservation projects. Mr Chisholm has written journal articles and presented papers at national and international conferences including the Canadian Institute of Planners (2002) and the Society for Ecological Restoration (2001). The paper that follows is based on a presentation that he gave at the international symposion on "The Natural City," Toronto, 23-25 June, 2004, sponsored by the University of Toronto's Division of the Environment, Institute for Environmental Studies, and the World Society for Ekistics.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.052 | 0.023 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".