Sharing and Adapting the Homeowner’s Handbook to Prepare for Natural Hazards
Bibliographic record
Abstract
In 2007, the University of Hawai‘i Sea Grant College Program produced the Homeowner’s Handbook to Prepare for Natural Hazards in response to the significant threat of hurricanes and a sense of the urgent need to help communities prepare. There are now 15 versions of the Handbook across the Sea Grant network, with over 189,250 copies printed in three languages. The Handbook helps prepare communities for natural hazard risk with best practices that are resilient, adaptive, and sustainable. Partnerships between scientific organizations, emergency managers, the private sector, and community groups have played key roles in developing, distributing, updating, and educating the public. A wide range of education activities—from adult outreach through seminars, webinars, emergency fairs, workshops, and continuing education courses to student and K–12 teaching resources to TV and media sharing—were developed for greater reach into the community. Going forward, programs have plans to include more information on climate change, to address not just homeowners but all residents, to encourage helping the vulnerable, and to work with emergency managers and partners to expand the range of education and outreach so that the “Whole Community” can be reached.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".