Multi-Level Governance of Climate Change Adaptation: The Role of Regional Partnerships in Canada and England
Bibliographic record
Abstract
Adaptation to climate change is widely recognized as a multi-level governance challenge because expected impacts and respective measures cut across governmental levels, sectors and societal domains. The present paper analyses the role of regional adaptation partnerships in Canada and England in the multi-level governance of climate change adaptation. We describe and compare three partnerships per country with regard to their evolution, membership and governing structures, coordination across levels and societal domains, and their adaptation activities and outputs. Although both partnership schemes represent new collaborative approaches, their genesis and governance differ. While the Canadian collaboratives are a government-centered approach that originated and partly operated top-down through a national programme for the period 2009-2012, the English partnerships follow a more pluralistic stakeholder-centred approach that evolved bottom-up already in the early 2000s. Both schemes have in common that they intermediate between governmental levels, foster networking between public and private actors, and eventually build adaptive capacities and inform adaptation policies. We conclude that regional adaptation partnerships represent a new governance approach that facilitates climate change adaptation, albeit with limits. Since state actors play(ed) key roles in both partnership schemes, they do not represent a new sphere of authority outside the state. Instead of blurring or destabilizing governmental levels they complement (and perhaps even stabilise) them with multi-level interactions.
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".