Cultural and Institutional Factors Driving Severe Repetitive Flood Losses: Insights From the Jersey Shore
Bibliographic record
Abstract
Decisions about how to respond to coastal flood hazards often involve disagreements over resource allocations. In the United States, large intergovernmental fiscal transfers have enabled rebuilding in areas that experience severe repetitive losses. This case study focuses on Ortley Beach, a barrier island neighborhood in Toms River, New Jersey, to examine the process of rebuilding after Superstorm Sandy in 2012 and competing visions for the future. A decade later, we conducted 32 key-informant interviews-including residents and local, state, and federal officials-to examine how values, worldviews, and beliefs shape preferences for coastal risk reduction strategies. A central debate was whether public resources should support staying or leaving the island. Key concerns included the economic impacts of strategies on household and public finances, the effectiveness of strategies to mitigate future flood damages, and fairness in the distribution of costs and responsibilities. Conflicts emerged in how stakeholders framed their preferences. Local officials tended to hold more individualistic-hierarchical worldviews, weaker beliefs in climate science, and favored actions to protect high-value properties to preserve the tax base while externalizing costs. In contrast, some residents and most state and federal officials held more community-egalitarian worldviews, stronger beliefs in climate science, and preferences for long-term adaptation strategies to reduce risk, including property buyouts. Responding to the primary concern about economic impacts, we recommend enhancing individual and local financial resilience to climate and political shocks by diversifying municipal revenue streams, encouraging proactive risk-based planning, exploring innovative insurance models, and better accounting for the long-term costs of rebuilding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".