Flood Governance: A multiple country comparison of stakeholder perceptions and aspirations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Flooding is routinely among the most disastrous annual events worldwide with extensive impacts on human wellbeing, economies and ecosystems. Thus, how decisions are made about floods (i.e. flood governance) is extremely important and evidence shows that it is changing, with non‐governmental actors (civil society and the private sector) becoming involved in new and sometimes hybrid governance arrangements. This study investigates how stakeholders perceive floods to be governed and how they believe decision‐making ought to occur, with the intent of determining to what extent changing governance is evident on the ground and how well (or poorly) it aligns with desired governance arrangements. Flood governance stakeholders were surveyed in five flood‐prone geographical areas from Australia, Canada, Italy, the Netherlands and Sweden. The findings suggest that a reconfiguration of flood governance is underway with relatively little consensus regarding the specific arrangements and mechanisms in place during this transitionary period. Across the five cases, stakeholders indicated that they wanted flood governance to be organized at multiple levels, with strong government involvement and with diverse actor groups, and through mechanisms that match the involvement of these actors, with a lack of desirability for some specific configurations involving the private sector in particular. There was little alignment between stakeholder perceptions of governance currently in place and their desired arrangements, except for government involvement. Future research directions highlight the importance of the inclusion of stakeholder perspectives in assessing flood governance, and following the transition in flood governance over time. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it