The hybrid governance of environmental transnational municipal networks: Lessons from 100 Resilient Cities
Bibliographic record
Abstract
Transnational Municipal Networks (TMNs) are increasing in size, scope and number on the global arena. They reflect a tendency for city governments to coordinate environmental action through networked forms of governance. In this article, we argue that a new generation of TMNs has entered the global scene to help cities steer their efforts to handle environmental issues. In contrast to the characteristics of older TMNs as public, inclusive, and self-governed, new-generation TMNs are influenced by private actors, they are exclusive, and employ enforcement mechanisms to secure the fulfilment of network goals. To underline the diversity of TMNs and thus better understand urban networked governance, we present a case study of the 100 Resilient Cities initiative covering its conduct in 2013–2019. Looking at its actor composition and membership terms, we identify a hybrid nature different from the one described in earlier literature on European TMNs primarily. This subscription to a hybrid form of governance calls for a larger discussion on the implications of this shift in governance type and on the extent to which hybridisation implies a shift of power from the public to the private sphere.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".