The Network Governance of Urban River Corridors
Bibliographic record
Abstract
Urban centers can provide important ecosystem services to society both through green spaces and river corridors. However, urbanization has impacted rivers, and as a consequence, there is increasing support for their sustainable management. The governance of urban river corridors reflects a trend toward stakeholder participation and partnership working in urban regeneration. The integration of ecological, social, and economic knowledge required for their sustainable management is achieved through networks of people and organizations that cross multiple sectors. However, little is known about the structure and function of such governance networks. We address this through a case study that explores the network structure of a multi-stakeholder collaboration tasked with developing a city-wide strategy for the sustainable management of urban river corridors in Sheffield, UK. We combine interpretive policy analysis and social network analysis to reveal the network structure and leadership characteristics of the group. We aim to explain why the group are having difficulty reaching a shared strategic vision for the river corridors and why they feel the group lacks representativeness. Our findings show that the network needs to become better connected to support an ongoing process of deliberation and negotiation for a shared vision. In addition, there is a limited diversity of stakeholders that will affect the legitimacy of the group and their ability to manage for a range of ecosystem services of benefit across society. We conclude that governance processes need to account for a diversity of actors that may change through time, and link regional and city networks to local interests.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".