Measuring the Degree of ‘Fit’ Within Social‐Ecological Systems to Support Local Flood Risk Decision‐Making
Bibliographic record
Abstract
ABSTRACT Effective social‐ecological fit is considered essential for properly managing social‐ecological systems. Despite this importance, the concept of social‐ecological fit lacks the following: clarity in scope and definition, a practical quantitative method to assess effectiveness, and methods capable of equally assessing the social and ecological factors within the system being managed. To address these knowledge gaps, we reviewed how social‐ecological fit has been conceptualised in the literature and then tested the use of Bayesian Belief networks and analysis to quantitatively assess “fit” using the case of flooding in the North Onslow saltmarsh region of Truro, Nova Scotia. The objective of this study was to assess which decision‐making choices would most likely reduce flood risk, and therefore achieve the best ‘fit’. Drawing from a combination of existing literature and local expert opinion, we identified the relevant factors influencing flood risk in the region, their relationship to each other and their combined relationship to local flood risk. Ice jam frequency, high tide frequency and dyke maintenance were found to have the most influence. The results of this study can be used to inform local flood‐risk‐related decision‐making in Truro and act as a model for quantitatively assessing social‐ecological fit in other risk management settings.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".