Engaging Vulnerable Populations in Multi-Level Stakeholder Collaborative Urban Adaptation Planning for Extreme Events and Climate Risks — A Case Study of East Boston USA
Bibliographic record
Abstract
Pressing challenges in urban adaptation planning to extreme events include: (1) involving vulnerable populations in the impacted area; and (2) employing a multi-level stakeholder collaborative process to build consensus for action. These processes become even more important as adaptive urban planning is recognized as an effective governance model for adaptation to climate change. In a case study of a low to moderate income community vulnerable to present and increased coastal storm surge flooding, the Supported Community Planning Process was employed because (a) most residents of East Boston affiliate primarily with their own local neighborhoods and (b) the residents need targeted expertise to help them understand some of the scientific and technical aspects of adaptation planning. Collaboration was necessary among three sets of critical stakeholders interested in adaptation strategies in East Boston — the local residents and small businesses, the City of Boston, and the agencies that provide infrastructure services — because some adaptation actions will collectively protect assets of all. The overall process occurred successfully because of positive, knowledgeable, and direct exchange of values and goals. The research illustrates how marginalized populations can be effectively engaged in urban adaptation planning, and how that process can be combined in multi-level stakeholder collaborative planning so that plans might be developed that meet multiple shared and individual goals in a cost-effective manner.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".