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Record W2044317619 · doi:10.5539/ijps.v6n3p32

Risk Assessment by British Children and Adults

2014· article· en· W2044317619 on OpenAlexvenueno aff
Michael J. Penkunas, Richard G. Coss, Susanne Shultz

Bibliographic record

VenueInternational Journal of Psychological Studies · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHippopotamusPsychologyDemographyAgency (philosophy)Developmental psychologySociologySocial science

Abstract

fetched live from OpenAlex

We present data illustrating how preschool-aged British children ranked the danger of different situations and adults rated various external causes of mortality. Ranks were calculated from 34 children’s ratings of dangers presented by eight scenarios using a three-dimensional diorama. Lion and hippopotamus figurines were presented to characterize historical threats with intentional agency. Children ranked the lion followed by the hippopotamus as presenting the greatest danger. When these ranks were pooled to reflect a general category of animal attacks, the children’s ranks failed to reflect the national statistics on childhood deaths. Adult ratings for the prevalence of 20 external causes of mortality in the general public were positively correlated with the actual frequency of mortality due to these causes. Nevertheless, adults were seen to underestimate their personal susceptibility to the same dangers. Children and adults differ in risk assessment based initially on early childhood predispositions, with experience altering risk assessment to match the local environment.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.526
Teacher spread0.469 · 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 designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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