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Record W2530431767 · doi:10.1002/fam.2391

Modelling and influencing human behaviour in fire

2016· article· en· W2530431767 on OpenAlexaffabout
S. Gwynne, Erica D. Kuligowski, Michael Kinsey, Lynn Hulse

Bibliographic record

VenueFire and Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsConceptual modelFoundation (evidence)Representation (politics)MajestyConceptual frameworkPoison controlOperations researchHuman factors and ergonomicsEngineeringManagement scienceComputer scienceSociologyPolitical scienceMedicineSocial scienceLawPoliticsMedical emergency

Abstract

fetched live from OpenAlex

Summary The purpose of this article is to present a conceptual model of human behaviour in fire and its impact on egress modelling, life safety analyses and evacuation procedures. This model is based on a theoretical framework of individual decision‐making and response to emergencies. From this foundation, the conceptual model is populated with behavioural statements or mini‐theories distilled from articles and authoritative reports describing emergency incidents, observations from within the field of evacuation analysis and studies of human behaviour in fire and other emergencies. The conceptual model is intended to guide the egress tool developer, user and practitioner to better account for human behaviour in their respective roles. It is contended that a more credible representation of the evacuee response, that incorporates the behavioural statements described, provides both theoretical and practical advantages. Copyright © 2016 Her Majesty the Queen in Right of Canada. Fire and Materials © 2016 John Wiley & Sons, Ltd.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.193

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.226
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations33
Published2016
Admission routes2
Has abstractyes

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