Enabling the Integration of Ecosystem Service-based Approaches into Planning Organizations: Municipal Natural Asset Management
Bibliographic record
Abstract
This research seeks to help bridge the gap between science and practice around the integration of ecosystem service-based approaches within municipal planning. This thesis identified enablers of organizational change needed to implement Municipal Natural Asset Management (MNAM) from an Ontario planning context utilizing a socio-technical system theory lens. Staff and decision-makers from municipalities and conservation authorities within the field of planning were interviewed with open-ended questions. \nEnablers were formed from top challenges, opportunities, and actions identified by participants. The seven top challenges, five top opportunities, and four top actions identified resulted in the formation of six enablers. Enablers that emerged were: 1) reducing a lack of knowledge of the value of ecological systems, 2) creating a clear action plan addressing resource constraints and municipal capacities, 3) increasing cross-jurisdictional and interdepartmental coordination, 4) leveraging Ontario policy frameworks and processes to enable MNAM implementation, 5) creating clear and concise tools and processes for MNAM implementation, and 6) finding a champion to help create and continue momentum of MNAM implementation. \nThe enablers addressed the top challenges while utilizing opportunities and actions identified during interviews. Results provided insight into enabling the implementation of MNAM within municipalities and tangible recommendations for implementing each enabler. It is recommended that to improve MNAM implementation success, enablers are strategically approached and implemented based on careful consideration of individual needs and capacities of municipalities.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".