Multi-level Governance and Place-Based Policy-Making for Climate Change Adaptation: The European Experience and Lessons for British Columbia
Bibliographic record
Abstract
While Canada is in the early stages of local community climate change adaptation efforts, lessons can be drawn from other multi-level governance systems which have also been grappling with these issues. The European Union (EU) is a prime example of how multi-level governance arrangements can be designed to enhance local community resilience. While many climate change studies in Canada have typically focused on the experiences of the US and or Australia, the EU experience in this area is also extensive and has many lessons from which Canadians and British Columbians can learn. The EU experience is examined here in order to see what kinds of lessons can be drawn from the multi-level governance arrangements prominent in Europe and whether key multi-level concepts such as subsidiarity developed there can be applied to Canada. The discussion below focuses in particular on local transnational networks such as ‘Eurocities’ and serves as a backdrop for possible new models for place-based climate change governance and local networks in BC
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".