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Record W2799762595 · doi:10.1177/1948550618768823

Distinguishing Dismissing From Fearful Attachment in the Association Between Closeness and Commitment

2018· article· en· W2799762595 on OpenAlexaff
Yoobin Park, Anik Debrot, Stephanie S. Spielmann, Samantha Joel, Emily A. Impett, Geoff MacDonald

Bibliographic record

VenueSocial Psychological and Personality Science · 2018
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClosenessAmbivalencePsychologyAssociation (psychology)Attachment theoryInsecure attachmentAnxietySocial psychologyDevelopmental psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

When avoidantly attached individuals are simultaneously high in attachment anxiety, they are inclined to experience strong internal conflicts between seeking and avoiding closeness. This research examined whether the extent to which closeness, assessed as the inclusion of other in the self (IOS), is associated with greater commitment varies within individuals high in attachment avoidance as a result of differences in ambivalence toward maintaining the relationship. In two studies ( N 1 = 1,604, N 2 = 2,271), we found that the positive association between IOS and commitment was significantly weaker when attachment avoidance was combined with high (vs. low) attachment anxiety. In Study 2, we found lingering relational ambivalence even at high levels of IOS among individuals simultaneously high in attachment avoidance and anxiety, which in turn was related to relatively low commitment. Our findings highlight the role of relational ambivalence in avoidants’ relationship functioning and the need to examine the interplay of the two attachment dimensions.

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)0.011
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
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.468
Teacher spread0.370 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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