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Record W2051930501 · doi:10.1556/jcep.2.2004.3-4.4

Impressions of Danger Influence Impressions of People: An Evolutionary Perspective on Individual and Collective Cognition

2004· article· en· W2051930501 on OpenAlexaff
Mark Schaller, Jason Faulkner, Hyewon Park, Luc Neuberg, T. Douglas Kenrick

Bibliographic record

VenueJournal of Cultural and Evolutionary Psychology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsIngroups and outgroupsPsychologySocial psychologyPerceptionCognitionPerspective (graphical)Set (abstract data type)Interpersonal communicationSocial cognitionCognitive psychology

Abstract

fetched live from OpenAlex

An evolutionary approach to social cognition yields novel hypotheses about the perception of people belonging to specific kinds of social categories. These implications are illustrated by empirical results linking the perceived threat of physical injury to stereotypical impressions of outgroups. We review a set of studies revealing several ways in which threat-connoting cues influence perceptions of ethnic outgroups and the individuals who belong to those outgroups. We also present new results that suggest additional implications of evolved danger-avoidance mechanisms on interpersonal communication and the persistence of cultural-level stereotypes about ethnic outgroups. The conceptual utility of an evolutionary approach is further illustrated by a parallel line of research linking the threat of disease to additional kinds of social perceptions and behaviors. Evolved danger-avoidance mechanisms appear to contribute in diverse ways to individual-level cognitive processes, as well as to culturally-shared collective beliefs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.403

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.395
Teacher spread0.352 · 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 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

Citations28
Published2004
Admission routes1
Has abstractyes

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