Adaptive Planning and Climate Focused Evaluation in an Era of Evolving Local Governance
Bibliographic record
Abstract
The interconnected relationship between cities and global climate change has led to the creation of a growing number of municipal climate change adaptation plans. Currently, there exist relatively few well known criteria on the best ways to evaluate these documents following their implementation. This study begins with a review of evaluation literature and policy reports drawn from four principle agencies considered to be at the forefront of climate change adaptation planning in Canada. Findings are then used to explore how the Cities of Toronto and New York have successfully incorporated evaluation criteria into their adaptation plans. Lessons are presented for both planning practitioners and local governments concerning the implementation of successful climate-focused evaluation criteria. Overall findings suggest that numerous tools exist for evaluating adaptation plans including important performance-based approaches. Agency commitment and persons assigned to conduct the evaluation as well as integration into an ongoing planning process were also found to be key success factors while evaluation outcomes were found to reflect the resources and expertise available given the present voluntary nature of climate plans.
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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 | 0.090 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".