The “Policy-Implementation” Gap in Natural Heritage System Planning: An Analysis of Mount Pleasant, Brampton, Ontario
Bibliographic record
Abstract
Does the existing natural landscape shape new communities? This study analyzed whether the natural heritage goals for new development as articulated in municipal planning policy were implemented successfully. Mount Pleasant, Brampton, Ontario, a recently-built community was used as a case study. This research reviewed how the planning process unfolded by analyzing the planning policies and studies produced through the development process. The lenses through which this analysis was performed are environmental planning approaches including landscape ecology, ecodesign, green infrastructure, and the ecosystem approach. There is a policy-implementation gap between landscape policy and planning practice, meaning that there is a failure to translate policies and plans into sustained on-ground outcomes for conservation. The analysis found that the new community did not reflect natural heritage policies. The Natural Heritage System planning process was not based on the existing natural features, rather, it was driven by maximizing developable area from the very beginning of the planning process. This paper concludes with suggestions about how planners can work towards closing this policy-implementation gap in order to create better conservation outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".