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The Propensity for Abusiveness Scale (PAS) as a Predictor of Affective Priming to Anticipated Intimate Conflict

2004· article· en· W2032294098 on OpenAlexaff
Lindsey A. Thomas, Donald G. Dutton

Bibliographic record

VenueJournal of Applied Social Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of British Columbia
Fundersnot available
KeywordsPsychologyAngerAnticipation (artificial intelligence)AnxietySocial psychologyInterpersonal communicationConflict resolutionScale (ratio)Priming (agriculture)Affect (linguistics)Developmental psychology

Abstract

fetched live from OpenAlex

The current study used the Propensity for Abusiveness Scale (PAS; Dutton, 1995) to predict emotional response to conflict among university students. The participants were 162 first‐ and second‐year undergraduate students at the University of British Columbia. Participants listened to taped conflict and filled out a battery of questionnaires. The PAS correlates significantly with pre‐anger, anxiety, subanger, and general arousal levels, suggesting that a type of emotional priming takes place when persons high on the PAS know they are about to be exposed to an interpersonal conflict. It is concluded that anticipation of intimate conflict appears to heighten negative affect in some young adults and that this response may interfere with future intimate conflict‐resolution strategies.

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)0.005
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.055
GPT teacher head0.432
Teacher spread0.377 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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