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Record W3195455764 · doi:10.62608/2158-0669.1257

Outgroup Prejudice from an Evolutionary Perspective: Survey Evidence from Europe

2015· article· en· W3195455764 on OpenAlexaff
Serdar Kaya

Bibliographic record

VenueJournal of International and Global Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOutgroupPrejudice (legal term)Perspective (graphical)PsychologyEvolutionary psychologySocial psychologyEvolutionary biologyBiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates the root causes of outgroup prejudice. The literature explains prejudice primarily as a result of the perception of threat or the lack of optimal intergroup contact. The literature also emphasizes that individuals who are prejudiced against one outgroup are more likely to be prejudiced against other outgroups as well. This study does not react to these established theories. Instead, it argues from an evolutionary social psychological perspective that the root cause of outgroup prejudice is an activated sense of distrust and caution. In ancestral environments, higher levels of distrust and caution helped humans better protect themselves and their offspring from outside dangers, especially that posed by other humans. Prejudice is thus a function of this general protective outlook rather than a function of the particular characteristics of outgroups. To test this hypothesis, the paper specifies six multilevel regression models and analyzes the factors that lead to prejudice against six salient minority groups: immigrants, Muslims, Jews, homosexuals, the Roma, and the people of different races. Data come primarily from the latest wave of the European Values Study, covering 43 European countries. In all six cases of outgroup prejudice, findings indicate a strong and consistent support for the proposed theoretical perspective.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.169
GPT teacher head0.467
Teacher spread0.298 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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