Identifying and analyzing barriers to ship-based evacuation planning using AIS data
Bibliographic record
Abstract
Coastal communities face distinct challenges in disaster response and crisis management, shaped by their unique geographical and demographic characteristics. This study departs from traditional emergency evacuation ideas by highlighting the use of Automatic Identification System (AIS) ship tracking data to improve evacuation planning for coastal populations. There remains a limited understanding of the barriers hindering humanitarian organizations (HOs) from fully integrating this technology into coastal community planning. This is evident in the scarce literature on the subject, with only one study exploring the feasibility of evacuation planning using AIS data. To examine the barriers to the adoption of AIS-based evacuation planning, Leavitt’s Diamond model was used as a conceptual framework. A mixed-method approach was employed to identify and analyze these barriers. Bhasan Char, a small island in the Bay of Bengal, serves as the research context for this study. The island is vulnerable to weather events, posing safety risks to its population of approximately 30,000 Rohingya refugees who fled from Myanmar. The study uniquely identifies governmental restrictions, training and education, and cultural sensitivity as primary drivers, emphasizing their critical role in shaping effective evacuation efforts. For instance, reforms to governmental restrictions are particularly pivotal, as they profoundly influence systemic challenges, including lack of coordination, policy ambiguity, absence of standard operating procedures, resource allocation inefficiencies, and emergency response limitations. By focusing on these primary drivers, this research provides a strategic framework for enhancing the effectiveness of evacuation planning efforts using AIS technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".