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Record W2110675324 · doi:10.22059/ijer.2010.48

The Communication of Disaster Information and Knowledge to Children Using Game Technique: The Disaster Awareness Game (DAG)

2009· article· en· W2110675324 on OpenAlexaff
Virginia Clerveaux, Balfour Spence

Bibliographic record

VenueTSpace · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsBrandon University
Fundersnot available
KeywordsVulnerability (computing)Reliability (semiconductor)Emergency managementFraternityTheme (computing)PsychologyKnowledge managementComputer securityComputer scienceEngineeringApplied psychologyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The specific vulnerability of children and by extension, the need to promote disaster awareness among children as an integral part of disaster risk-reduction strategies is an emergent theme in the disaster management fraternity. The challenge however, is in the design of awarenesspromotion tools that are relevant to and appropriate for the specific learning needs of children. The Disaster Awareness Game (DAG) on which this paper is based has been designed to address this challenge. Preliminary testing of the Game among Caribbean school children suggests that it is appropriate for and effective in rising levels of awareness and consequent behaviour of children in disaster situations. In light of the preliminary nature of these results further testing of the DAG is imperative if confirmation of its reliability is to be obtained.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.373
Teacher spread0.351 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2009
Admission routes1
Has abstractyes

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