Fit for performance? Examining the complexities of flood planning in relationship to effectiveness
Bibliographic record
Abstract
Social-ecological (SE) fit has been posited as a prerequisite for decision-making performance. However, empirical research linking indicators of SE fit to performance are limited. Current studies rarely consider how multiple overlapping interdependencies that constitute social-ecological systems (SES) influence fit and performance. This research investigates flood planning to probe the relationship between SE fit and functional performance. We incorporate aspects of complexity from the ecological system through interconnected sub-basins and from the collective action problem through interdependent functions. Applying a multi-level network approach, we assessed how patterns of collaboration believed to support positive outcomes in social-ecological systems (i.e., SE fit) impact task performance when accounting for different SE fit challenges. When actors were working in the same sub-basin, collaboration that aligned to interdependent functions did not influence performance. When actors collaborated across sub-basins, collaboration that aligned with interdependent functions enhanced performance. Our findings highlight that SE fit is crucial for enhancing performance specifically when contextual factors will increase the transaction cost of collaborative relationships.
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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 | 0.010 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".