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Record W2956135828 · doi:10.1177/0305735618792404

The terror management effects of a disaster song

2019· article· en· W2956135828 on OpenAlexaff
Laura Higgins, Peter D. MacIntyre, Jessica Ross, Heather Sparling

Bibliographic record

VenuePsychology of Music · 2019
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsCape Breton University
Fundersnot available
KeywordsActive listeningPsychologySocial psychologyRanking (information retrieval)Developmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Following terror management theory (TMT), we hypothesized that listening to a disaster song could increase cultural worldview defenses in a manner similar to the mortality-stimulating essay typically used in TMT research. Participants were divided into four groups. Two of the groups received death-related themes: one wrote an essay about dying and the other group heard a song about men who died in a shipwreck. The other two groups received pain-related stimuli: one wrote an essay about dental pain and the other heard a song about a migrant worker’s painful separation from family. Dependent variables examined pro-social behavior, ranking one’s country, children, and emotions. Results showed similar effects for the mortality-stimulating essay and the disaster song on two variables: ranking one’s country in the world and the importance of having children. In addition, compared to the pain-of-separation song, the disaster song produced significantly more negative and less positive emotion ratings; the emotion ratings of the essay groups did not differ significantly. Results show that a disaster song can produce effects similar to those that have been observed for a mortality-stimulating essay. Further, the effects of disaster songs may extend to strengthening cultural worldview defenses.

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 score0.806
Threshold uncertainty score0.740

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.306
Teacher spread0.295 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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