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Record W4395680568 · doi:10.5670/oceanog.2024.231

Sharing and Adapting the Homeowner’s Handbook to Prepare for Natural Hazards

2024· article· en· W4395680568 on OpenAlex
Dennis J. Hwang, Kanesa Duncan Seraphin, Darren K. Okimoto, Cindy Knapman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOceanography · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersStrongHawai'i Sea Grant, University of Hawai'iCanadian Institute for Theoretical Astrophysics
KeywordsOutreachPublic relationsWork (physics)Natural hazardNatural disasterCommunity educationHazardBusinessNatural resourcePolitical scienceEngineeringSociologyGeographyPedagogyMeteorologyEcology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.306
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it