Spatial Decision Support in a Post-disaster Environment: A Community-Focused Approach
Bibliographic record
Abstract
Disasters, by definition, are low-frequency, high-impact events. Because of the infrequent occurrence and dynamic conditions of such events, researchers do not often have the ability to thoroughly study their effects. However, as population distributions and densities place more people at risk of experiencing a disaster, more complete investigations of these events are needed. Recovery, the post-disaster phase of returning to “normalcy,” is one understudied aspect. Gaining a better understanding of this process has important implications for the well-being of disaster-affected people and equitable redevelopment of affected places. This article focuses on one aspect of recovery: spatial decision support for communities. Though the literature on community mapping discusses a variety of cases in which geographic information systems (GIS) are used as a tool to engage the public on a variety of issues, it has yet to address concerns in a post-disaster environment. Furthermore, existing research in geographic information science (GISc) for disasters has only limited dealings with community-focused mapping. The result is that community information needs in a post-disaster environment have not been recognized. Based on existing literature and empirical observations from post-Katrina Louisiana, this article addresses the spatial component of these information needs and how GISc can provide decision-support tools in a post-disaster environment.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".