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Record W2556274001 · doi:10.1080/21665044.2016.1263141

Fear-related behaviors in situations of mass threat

2016· article· en· W2556274001 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDisaster Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute on Drug Abuse
KeywordsHarmPsychologyPopulationSocial psychologyHazardMedicineEnvironmental health

Abstract

fetched live from OpenAlex

. Disaster case scenarios are presented to illustrate how fear-related behaviors operate when a potentially traumatic event threatens or endangers the physical and/or psychological health, wellbeing, and integrity of a population. Fear-related behaviors may exacerbate harm, leading to severe and sometimes deadly consequences as exemplified by the Ebola pandemic in West Africa. Alternatively, fear-related behaviors may be channeled in a constructive and life-saving manner to motivate protective behaviors that mitigate or prevent harm, depending upon the nature of the threat scenario that is confronting the population. The interaction between fear-related behaviors and a mass threat is related to the type, magnitude, and consequences of the population encounter with the threat or hazard. The expression of FRBs, ranging from risk exacerbation to risk reduction, is also influenced by such properties of the threat as predictability, familiarity, controllability, preventability, and intentionality.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.050
GPT teacher head0.428
Teacher spread0.378 · 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