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Record W2912256041 · doi:10.1002/ejsp.2578

“Digging in” or “Giving in”: Attachment‐related threat moderates the association between attachment orientation and reactions to conflict

2019· article· en· W2912256041 on OpenAlexafffund
Tara K. MacDonald, Valerie M. Wood, Leandre R. Fabrigar

Bibliographic record

VenueEuropean Journal of Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAnxietyAttachment theoryInsecure attachmentAssociation (psychology)Social psychologyDominance (genetics)Developmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Abstract Prior research suggests that individuals higher in attachment anxiety react to conflict in a more hostile manner than those lower in attachment anxiety. Although less pronounced, there is also evidence that attachment anxiety is associated with submissive behavior in conflict. Thus, the literature presents a paradox, as attachment anxiety is associated with both domineering and submissive responses to relationship conflict. We proposed that attachment‐related threat moderates the effects of attachment orientations on conflict behavior, such that under conditions of low threat attachment anxiety would be associated with dominance, whereas under conditions of high threat attachment anxiety would be associated with submission. Further, we expected that this interaction between attachment anxiety and threat condition would be stronger for individuals lower in attachment avoidance, relative to those higher in avoidance. We found support for our hypotheses, such that attachment anxiety, attachment avoidance, and threat interacted to predict responses to relationship conflict.

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 score0.004
Threshold uncertainty score0.013

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.416
Teacher spread0.371 · 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
Published2019
Admission routes2
Has abstractyes

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