Ecosystem Perceptions in Flood Prone Areas: A Typology and Its Relationship to Preferences for Governance
Bibliographic record
Abstract
A shift appears to be occurring in thinking about flooding, from a resistance-based approach to one of resilience. Accordingly, how stakeholders in flood-prone regions perceive the system and its governance are salient questions. This study queried stakeholders’ internal representations of ecosystems (resistance- or resilience-based), preferences for governance actors and mechanisms for flooding, and the relationship between them in five different regions of the world. The influence of personal experience on these variables was also assessed. Most respondents aligned themselves with a resilience-based approach in relation to system connectedness and response to disturbance; however, respondents were almost evenly split between resistance- and resilience-based approaches when considering system management. Responses generally were considered to hold for other disturbances as well. There was no clear relationship between internal representations and preferences for governance actors or mechanisms. Respondents generally favoured actor combinations that included governments and mechanism combinations that included regulations and policies. Those who had personal experience with flooding tended to align themselves with a resilience-based internal representation of system management, but personal experience showed no clear relationship with governance preferences. The findings support an evolutionary perspective of flood management where emerging paradigms enhance preceding ones, and prompt a critical discussion about the universality of resilience as a framing construct.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".