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Record W2147358192 · doi:10.1177/0261927x04266813

Adolescent-Parent Verbal Conflict

2004· article· en· W2147358192 on OpenAlexaff
Sherry L. Beaumont, Shannon L. Wagner

Bibliographic record

VenueJournal of Language and Social Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsDisgustPsychologyContemptDevelopmental psychologyStyle (visual arts)DaughterPerceptionSocial psychologyEmotional expressionSocial perceptionAnger

Abstract

fetched live from OpenAlex

A total of 94 adolescents (M age = 14.2 years) participated in 20-minute hypothetical conflict discussions with either their mothers or fathers (24 daughter-mother, 22 son-mother, 25 daughter-father, 23 son-father dyads). Audiotaped conversations were coded for speakers’ conversational style (overlaps between turns, simultaneous speech, and successful interruptions) and hostile emotional expressions (i.e., disgust/contempt). Adolescents used a conversational style that included more overlaps, simultaneous speech and successful interruptions than their parents, with the greatest differences in styles found for adolescents and their mothers. Adolescents’ and parents’ conversational styles and expressions of disgust were analyzed in modeling procedures in an attempt to predict selfreported perceptions of adolescent-parent conflict. Results revealed that adolescents’ rates of disgust were positively predicted from both the degree of difference in the adolescent’s and parent’s conversational styles and from the parent’s rates of disgust expressions. In turn, adolescents’ expressions of disgust were found to positively predict adolescents’ perceptions of levels of relationship conflict with their parents.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.352
Teacher spread0.317 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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