From Policy Challenge to Implementation Strategy: Enabling Strategies for Network Governance of Urban Resilience
Bibliographic record
Abstract
What transformations do municipal administrations implement to enact a resilience policy? This article responds to this question from a comparative perspective by analyzing enabling and impeding mechanisms developed in the cities of Montreal (Canada) and London (UK) as they establish their strategies. Collaborative network governance and institutional work mechanisms used in Montreal and London are analyzed in connection with the influence of macro‐ and micro‐contextual elements under which a network can resiliently manage risk and crises. In both cases, the development of resilience emerges from their emergency management structures, as units in charge try to animate their new area of responsibility through collaborative governance. As a siloed approach this is embedded in daily routines, organizations with limited resources focused on shared motivation and values, collaboration across organizational boundaries and creation of joint capacity to implement resilience. This transformative process concerns the organization in charge of resilience in the municipal administration and the wider network that they build and animate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".