Assessing social-ecological fit of flood planning governance
Bibliographic record
Abstract
Social-ecological fit demands that governance systems align with and function at the appropriate scales of social and ecological processes being governed. While multilevel social-ecological network analysis has been applied to assess fit in various contexts, it has not yet been applied to understand transboundary flood planning. We investigate the social-ecological fit of collaborative flood planning efforts in the St. John River Basin, located in New Brunswick and Quebec in Canada and Maine in the United States, focusing on two social-ecological fit challenges: shared management of ecological resources and management of interconnected resources. Our results displayed organizations have a tendency to collaborate with others located in the same sub-sub-basin and not with those working in different sub-sub-basins, indicating limited social-ecological fit of the collaboration network to flooding at the basin scale. Qualitative analysis identified collaboration provided increased knowledge and technical resources to engage in flood planning, but it was hindered by a lack of financial resources, time constraints, and a lack of shared commitment. Collaborative relationships among organizations working in different sub-sub-basins are essential for cohesive flood planning at the basin level, and in this case, there is potential for an increase in collaboration among ecological neighbors to govern for ecological connectivity.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".