Adapting adaptation: the English eco-town initiative as governance process
Bibliographic record
Abstract
Climate change adaptation and mitigation have become key policy drivers in the UK under its Climate Change Act of 2008.At the same time, urbanization has been high on the agenda, given the pressing need for substantial additional housing, particularly in southeast England.These twin policy objectives were brought together in the UK government's 'eco-town' initiative for England launched in 2007, which has since resulted in four eco-town projects currently under development.We critically analyze the eco-town initiative's policy evolution and early planning phase from a multilevel governance perspective by focusing on the following two interrelated aspects: (1) the evolving governance structures and resulting dynamics arising from the development of the eco-town initiative at UK governmental level, and the subsequent partial devolution to local stakeholders, including local authorities and nongovernmental actors, under the new 'localism' agenda; and (2) the effect of these governance dynamics on the conceptual and practical approach to adaptation through the emerging eco-town projects.As such, we problematize the impact of multilevel governance relations, and competing governance strategies and leadership, on shaping eco-town and related adaptation strategies and practice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".