Characterizing social barriers to nature-based coastal adaptation approaches
Bibliographic record
Abstract
Sea levels are rising faster than ever, limiting the effectiveness of hard infrastructure-based coastal protection. Scientists and policymakers are exploring alternative adaptation approaches that use nature's capacity to buffer flooding and erosion – a strategy we refer to as nature-based coastal adaption (NbCA). They involve changes from site-level to landscape scales, and employ design interventions and change in land use behaviour. They can have significant social implications and therefore encounter social barriers with unique underlying characteristics. Since NbCA is a relatively new adaptation approach, empirically driven knowledge about its barriers is less known. To help overcome this knowledge gap, we systematically searched empirical studies and characterized and synthesized social barriers to implementing NbCA. They include institutional, governance, economic, knowledge and informational, cultural, political, and psychological barriers. The properties of these barriers help us to see that: i) barriers are interconnected; ii) managing barriers requires alignment of key actors and targeted resources across scales; and iii) psychological and institutional barriers can be trapped in path-dependency. We conclude that attention to the institutional and social-psychological barriers can help manage other barriers.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".