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Record W2619407786 · doi:10.1002/ejsp.2251

Ask and you might receive: The actor–partner interdependence model approach to estimating cultural and gender variations in social support

2017· article· en· W2619407786 on OpenAlexaff
Biru Zhou, Dara Heather, Alessia Di Cesare, Andrew G. Ryder

Bibliographic record

VenueEuropean Journal of Social Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill UniversityConcordia UniversityMcGill University Health CentreJewish General HospitalMontreal Children's Hospital
Fundersnot available
KeywordsFriendshipPsychologySocial psychologySocial supportPartner effectsEmpirical researchDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract As an essential part of close relationships, social support is a dynamic interactive process. This paper aims to simultaneously investigate social support‐seeking and provision behaviours using the Actor–Partner Interdependence Model (APIM). Ninety‐two friendship dyads participated in this study. Supportive versus negative friendship qualities were used to predict different support‐seeking and support‐provision behaviours during an experimental task. Cultural and gender variations were also examined. Results showed that self‐reported friendship qualities influence support‐seeking and provision behaviours intrapersonally and interpersonally. Female participants were more likely to provide emotion‐focused support than were male participants. After accounting for friendship qualities in the dyads, there was no evidence of cultural group differences on support‐seeking or provision behaviours among same‐sex friends. These results demonstrate the conceptual and empirical advantages of using APIM to unpack cultural and gender variations in social support processes.

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)0.005
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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.147
GPT teacher head0.472
Teacher spread0.324 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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