Evaluating Children’s Learning of Adaptive Response Capacities from ShakeOut, an Earthquake and Tsunami Drill in Two Washington State School Districts
Bibliographic record
Abstract
Abstract In 2012, Washington state participated in ShakeOut, an annual, one-day event that encourages residents to practice “drop, cover and hold on” drills for earthquakes and evacuation for tsunamis. To better understand the role of school drills in improving individual and community resilience to disasters, this evaluation examined the effectiveness of the ShakeOut drills in improving or maintaining children’s accurate risk perceptions and adaptive response capacities for earthquakes and tsunamis. Using matched pretest and posttest questionnaires, the analysis examined both population level and individual differences in children’s knowledge and scenario-based knowledge application before and after ShakeOut. Children demonstrated high levels of correct knowledge of protective actions for earthquakes and tsunamis both before and after ShakeOut. However, the findings indicate that significant portions of children have varying levels of knowledge of the causes of injury and approximately a third of children chose an incorrect action or indicated uncertainty in scenarios not commonly practiced in school earthquake drills. Also, more than a quarter of children were not aware they practiced vertical evacuation procedures for a tsunami during ShakeOut. Children would benefit from practice for different scenarios, such as when they are outside or traveling between classes, and explicit lessons on protective actions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".