A sixfold urban design framework to assess climate resilience: Generative transformation in Negril, Jamaica
Bibliographic record
Abstract
The uncertainty of climate change's impacts hinders adaptation actions, particularly micro-scale urban design interventions. This paper proposes a sixfold urban design framework to assess and enhance the resilience of urban form to climate change, where urban form refers to the patterns of streets, buildings, and land uses. The framework is then applied to Long Bay in Negril, Jamaica-a coastal area that incorporates the complex interactions between urbanization and a highly vulnerable socio-ecological system to climate change-related hazards, primarily sea-level rise. Empirical evidence from 19 in-depth interviews with planning and design professionals and development actors, in situ observations, and morphological analyses reveal that Long Bay's current adaptation strategies heavily rely on bounce-back resilience measures that predominantly consider the impacts of extreme climatic events rather than slow-onset ones. Such strategies abet current tourism-driven development patterns while overlooking Long Bay's inherent abilities for generative transformation and incremental changes to meet climatic uncertainty. Instead, this study's findings highlight how generative urban form transformation would better equip Long Bay to cope with future uncertainty-climatic or other.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".