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Record W1610837822 · doi:10.1111/bjso.12118

Finding death in meaninglessness: Evidence that death‐thought accessibility increases in response to meaning threats

2015· article· en· W1610837822 on OpenAlexaff
David Webber, Rui Zhang, Jeff Schimel, Jamin Blatter

Bibliographic record

VenueBritish Journal of Social Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of AlbertaYork University
Fundersnot available
KeywordsMeaning (existential)PsychologySocial psychologyCompensation (psychology)EpistemologyPsychotherapistPhilosophy

Abstract

fetched live from OpenAlex

The meaning maintenance model proposes that violations to one's expectations will cause subsequent meaning restoration. In attempts to distinguish meaning maintenance mechanisms from mechanisms of terror management, previous research has failed to find increased death-thought accessibility (DTA) in response to various meaning threats. The present research suggests that this failure may have resulted from methodological differences in the way researchers measured DTA. Studies 1a and 1b found that by replacing this method with a standard method employed when studying worldview and self-esteem threats, DTA increased in response to two different meaning violations. Study 2 found increased DTA, but only among individuals high in personal need for structure, when using this standard DTA procedure, but not when using the procedure taken from previous meaning maintenance studies. Interestingly, these studies did not find increased meaning restoration, so an additional study (Study 3) was designed to provide a theoretically informed examination of this null effect. A meaning restoration effect was observed after removing the standard DTA assessment procedure, but only among participants high in personal need for structure. Implications for the threat-compensation literature are discussed.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.176
GPT teacher head0.439
Teacher spread0.264 · 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.

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

Citations37
Published2015
Admission routes1
Has abstractyes

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