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Record W1966190768 · doi:10.2190/em.30.2.e

Serene Arts: The Effect of Personal Unsettledness and of Paintings' Narrative Structure on Personality

2012· article· en· W1966190768 on OpenAlexaff
Maja Djikic, Keith Oatley, Jordan B. Peterson

Bibliographic record

VenueEmpirical Studies of the Arts · 2012
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativePersonalityPaintingPsychologyCoherence (philosophical gambling strategy)Big Five personality traitsSet (abstract data type)Social psychologyAestheticsArtVisual artsLiterature

Abstract

fetched live from OpenAlex

Previous research has demonstrated that art can produce some variation in self-reported personality traits. The present experiment addressed two questions. First, does visual art cause greater fluctuations in personality for unsettled or serene individuals, and second, do unsettled individuals respond more to art as a function of its narrative structure? Participants ( N = 61) completed a set of questionnaires, then viewed a series of paintings, Giotto's Seven Vices, either unmodified to exhibit high narrative structure, or modified to exhibit low narrative structure, and then filled another set of questionnaires. The results show that unsettled individuals experienced significantly less fluctuation of personality across conditions, and that in the condition of low narrative coherence, serene individuals experienced significantly more personality fluctuations than unsettled individuals. The results suggest that unsettled persons may need more narrative coherence in the art they engage with, while serene individuals may remain open to less-structured and more ambiguous art.

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 score0.002
Threshold uncertainty score0.007

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.412
Teacher spread0.329 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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