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Record W2107454398 · doi:10.1002/ejsp.482

Mortality salience and religion: divergent effects on the defense of cultural worldviews for the religious and the non‐religious

2008· article· en· W2107454398 on OpenAlexaffabout
Ara Norenzayan, Ilan Dar‐Nimrod, Ian Hansen, Travis Proulx

Bibliographic record

VenueEuropean Journal of Social Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMortality salienceSalience (neuroscience)PsychologyTerror management theoryReligious identitySocial psychologyExistentialismReligious orientationReligious beliefSalientReligiosityLawEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Religious and non‐religious individuals differ in their core beliefs. The religious endorse a supernatural, divinely inspired view of the world, while the non‐religious hold largely secular worldviews. As a result they may respond differently to existential threats. Three studies confirmed this prediction. After a mortality salience (MS) or control prime, Canadian participants read, and responded to, an essay hostile to Western civilization, allegedly written by a radical Muslim student. Results indicated that the non‐religious reliably showed the conventional cultural worldview defense by devaluating the content of the message and decreasing support for the civil rights of anti‐Western individuals when death was salient. No such effect was found for the religious. Religious and non‐religious participants did not differ in self‐esteem levels or in death‐thought accessibility. These results suggest that a religious stance among believers plays a defensive role against the awareness of death. Copyright © 2008 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 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)0.006
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.338
Teacher spread0.296 · 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

Citations89
Published2008
Admission routes2
Has abstractyes

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