Review of resilience hubs and associated transportation needs
Bibliographic record
Abstract
Rapid urban growth and the devastating impacts of disasters and emergencies have challenged infrastructure and social systems in many communities. Recently, the nascent concept of “resilience hubs” has emerged to help communities overcome these challenges and improve well-being during disasters and everyday conditions. This paper provides an early conceptual understanding of resilience hubs, in particular their associated transportation needs, through a comprehensive literature review. The review identified characteristics and needs for planning hubs by focusing on their current definitions and related concepts (e.g., evacuation shelters, mobility hubs). In all, the review identified that resilience hubs could be a successful tool for communities in addressing the important needs of residents, evacuees, and survivors. However, we found that the placement of hubs is not methodical or optimized, and hubs have yet to be evaluated using metrics or key performance indicators. Critically, most literature and examples of resilience hubs fail to consider: 1) how people and relief supplies will travel to/from hubs, or 2) potential transportation services that could be offered by hubs. We recommend that programs that identify, design, and create resilience hubs should emphasize mechanisms for providing reliable and equitable transportation for people and relief supplies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".