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Record W2967855521 · doi:10.1177/0305735619864630

Maladaptive personality and psychopathy dimensions as predictors of music and movie preferences in US adults

2019· article· en· W2967855521 on OpenAlexaff
Pavel S. Blagov, Kristi Von Handorf, Alan T. Pugh, Morgan G Walker

Bibliographic record

VenuePsychology of Music · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyPsychopathyPersonalityEntertainmentBig Five personality traitsTraitSensation seekingDark triadSocial psychology

Abstract

fetched live from OpenAlex

We link modern conceptualizations of maladaptive personality and psychopathy dimensions to music and movie genre preferences. Participants, N = 379, completed the Personality Inventory (5th ed.; PID-5), Triarchic Psychopathy Measure (TriPM), and music and movie preferences questionnaires. The structure of some, but not all, music preferences factors aligned with previous reports. Overall, maladaptive traits had meaningful, albeit modest links to entertainment media preferences, but not to the kinds of intense or rebellious music genres sometimes labeled as “problem” entertainment in prior literature. Support emerged for several a priori hypotheses, but some predictions based on the three- and five-factor normal personality trait and entertainment preferences literature did not generalize to a five-factor formulation of maladaptive personality. We discuss the findings’ implications and several likely sources of inconsistencies in the literature on music and movie preferences and personality.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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