Explaining communities' adaptation strategies for coastal flood risk: Vulnerability and institutional factors
Bibliographic record
Abstract
Abstract Increasing coastal flood risk has prompted a proliferation of cities that are adopting risk reduction and adaptation tools. This article inquires into what types of tools local governments tend to adopt for managing coastal flood risk and the factors that may be influencing these choices; in particular, factors related to hazard vulnerability and institutional capacity. Focusing on 40 diverse coastal communities in a study region in Canada, the study utilised data from the communities' Official Community Plans to characterise their approaches to managing coastal flood risk in terms of land use regulations, construction specifications, and/or structural flood protection tools. The data revealed considerable diversity in the portfolio of tools that the communities have adopted. Tool adoption was found to correlate strongly with hazard vulnerability; that is, communities with similar physical and socio‐economic vulnerability conditions tended to take similar adaptation actions. For example, established communities with highly urbanised coastlines tended to rely on structural flood protection while suburban communities with semi‐developed coastlines predominantly utilised land use regulations. Institutional factors such as resource availability and local leadership, which were operationalised using survey data, exhibited surprisingly little correlation with the types of tools that communities adopted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".