Lament for a network? Cities and networked climate governance in Canada
Bibliographic record
Abstract
There is substantial evidence that the global governance of climate change must pass through cities. While formal networks offer cities a means of generating effects that extend beyond their own borders, it remains unclear as to whether such networks can address collective action barriers and implementation gaps. City-networks, after all, are limited in their efforts to govern and must rely on information, service provision, and soft forms of coercion if they are to steer their members past these considerable challenges. This article contributes to extant efforts to assess their ability to do so by addressing two gaps in the literature. First, the article focuses on the Partners for Climate Protection (PCP), a city-network that has received little attention to date. Second, through analysis of two Canadian cities (Toronto and Winnipeg), the article provides an empirical illustration of the limitations of network authority and influence, and offers some thoughts on what this means for networked urban climate governance in Canada and beyond.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".