Examining municipal response to a provincial climate action planning mandate in British Columbia, Canada
Bibliographic record
Abstract
The provincial legislature of British Columbia (B.C.), Canada, recently enacted Bill 27, which requires all municipal official community plans to contain greenhouse gas emissions reduction targets and associated policies by the end of May 2010. While the legislation is unique by North American standards in its mandate, it lacks particular design features that scholars believe to be critical for fostering compliance. To examine municipal compliance with Bill 27, we address two research questions: what per cent of B.C. municipalities adopted targets by the legislated deadline, and which factors explain variation in target adoption across municipalities? To help answer these questions, we utilise univariate descriptive statistics, bivariate analyses (including mean comparisons and correlation analysis), and binary logistic regression analysis. We find that nearly two-thirds of municipalities adopted targets by the deadline, and that target adoption across municipalities varies with particular municipal characteristics. Our findings highlight the importance of crafting legislation in a strategic fashion in order to maximise effectiveness, and the potential need for provincial governments to target particular sub-populations for additional education and assistance regarding climate action planning. We suggest directions for future research, including additional analysis of the content of adopted targets and associated policies by B.C. 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 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".