HurricaneLog: A serious game for data collection and analysis of hurricane preparedness and response operations
Bibliographic record
Abstract
Hurricanes are destructive natural disasters that frequently cause significant damage and disrupt communities. An effective response relies on the swift actions of government and humanitarian organizations, and logistics play a crucial role in ensuring timely aid delivery. However, the complexity and unpredictability of disaster management can hinder decision making, often resulting in unintended outcomes. Data collection during emergency operations is challenging, and post-event surveys are prone to recall bias, creating barriers to obtaining valuable data to improve decision making. To address these challenges, we introduce HurricaneLog , a serious game that simulates disaster preparedness and response in a hurricane-prone region. By replicating realistic hurricane scenarios based on historical data, HurricaneLog provides a simulated environment to practice decision making and collect granular data, improving training for humanitarian logistics professionals and apprentices. This study contributes by introducing a publicly available game and proposing a methodological framework to analyze participants’ decisions and evaluate the effectiveness of the strategy. Detailed findings from an experiment involving 86 participants reveal decision-making patterns and provide practical evidence on disaster management strategies.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".