Overcoming obstacles to parkland acquisition: an analysis of the City of Toronto's parkland acquisition tools, why they have failed the downtown, and what can be done about it
Bibliographic record
Abstract
Since the mid-1900s, municipal parkland acquisition in North America has occurred largely through the development process. This paper asks how well Toronto’s existing acquisition tools and policies are meeting the needs of present and future populations in the downtown. Research was conducted through a review of the literature, policy and budgetary analysis, and twelve key informant interviews with municipal parks staff, councillors, developers, a planning consultant, and community advocates. The research finds that there are many factors that have impeded parkland acquisition in the downtown, including lack of a strong planning framework, historical disbursal of cash-in-lieu funds city-wide, governance challenges, underfunding of existing operations, limited land supply, a perception of land as overpriced, inability to purchase at market price and in a timely fashion, and limited knowledge of existing tools. The paper acknowledges what has worked and concludes with recommendations to improve the City’s parkland acquisition and development framework.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".