Carless and Special Needs Evacuation Planning
Bibliographic record
Abstract
For this review, we included a wide range of literature related to emergency preparedness planning and information on the evacuation of carless residents, including minority, low income, elderly, disabled, and residents with limited mobility and health problems. The review includes sources that highlight best practices, and identify areas of weakness within the field of emergency preparedness with respect to the target population of this study. This review discusses different needs for different types of natural and human-induced disasters. It also discusses the role for an integrated, multimodal approach for evacuation planning to assist with evacuating people in the most efficient manner possible. This literature review serves to characterize the current state of thinking and practice on the subject of carless and special needs evacuation planning. The focus of the literature review is on the role of government and public agencies. Overall, the literature related to carless evacuation planning is multidisciplinary and wide ranging. The events surrounding Hurricane Katrina motivated this review of existing research and provided an opportunity to synthesize other earlier related research. The process is important for finding gaps in the contemporary understanding of these issues, especially given more recent evacuations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".