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Record W2062882240 · doi:10.1177/0146167206291476

Voicing Conflict: Preferred Conflict Strategies Among Incremental and Entity Theorists

2006· article· en· W2062882240 on OpenAlexaff
Lara K. Kammrath, Carol S. Dweck

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2006
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsychologySocial psychologyInterpersonal communicationPersonalityFeelingVoiceDivergence (linguistics)Implicit personality theoryInterpersonal relationshipConflict resolution researchConflict theoriesConflict resolutionSociology

Abstract

fetched live from OpenAlex

The way individuals choose to handle their feelings during interpersonal conflicts has important consequences for relationship outcomes. In this article, the authors predict and find evidence that people's implicit theory of personality is an important predictor of conflict behavior following a relationship transgression. Incremental theorists, who believe personality can change and improve, were likely to voice their displeasure with others openly and constructively during conflicts. Entity theorists, who believe personality is fundamentally fixed, were less likely to voice their dissatisfactions directly. These patterns were observed in both a retrospective study of conflict in dating relationships (Study 1) and a prospective study of daily conflict experiences (Study 2). Study 2 revealed that the divergence between incremental and entity theorists was increasingly pronounced as conflicts increased in severity: the higher the stakes the stronger the effect.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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

Citations129
Published2006
Admission routes1
Has abstractyes

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