The Cooksville Creek Parkland Acquisition project: Planning a green space retrofit in Mississauga, Ontario
Bibliographic record
Abstract
The push to intensify in the GTA can put development pressures on community green spaces that provide vital public health and environmental services. As cities grow, planners will need to provide adequate green space for the growing population and to do so, they may need to re-arrange, or ‘retrofit’, land-uses to insert green spaces in the landscape. Following this, I examine the policy framework (including Provincial, Regional and municipal policies and plans) and tools that enable retrofits for green spaces in the GTA. I then investigate a case study, the Cooksville Creek Parkland Acquisition, a green space retrofit in Mississauga, Ontario to see how one municipality has approached a retrofit project. To better understand how the project is being implemented, I explore the role of four ‘implementation factors’: actors; values and visions; governance structures and decision-making processes; and policies and strategies. I found that while each of these factors interacted with each other, and that each was important in moving the Cooksville Project forward, the role of the actor (a city Councillor in this case) was especially pertinent—as well as two additional factors that were not included in the original framework: the economic and natural environment context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".