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Record W2504083995 · doi:10.1177/0265407516660388

Let’s stay home and watch TV

2016· article· en· W2504083995 on OpenAlexaff
Sarah Gomillion, Shira Gabriel, Kerry Kawakami, Ariana F. Young

Bibliographic record

VenueJournal of Social and Personal Relationships · 2016
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsYork University
FundersNational Science Foundation
KeywordsClosenessRomancePsychologySocial psychologyQuality (philosophy)Identity (music)Social mediaSocial network (sociolinguistics)Computer scienceWorld Wide WebAesthetics

Abstract

fetched live from OpenAlex

Sharing a social identity is a key component of interdependence in romantic relationships. In particular, sharing a social network of friends and family members with a romantic partner enhances relationship quality, but maintaining an integrated social network is not always possible. When people lack a shared circle of friends with their partners, sharing media like TV shows, books, and movies with partners may compensate for this deficit and restore closeness. Two studies examined the influence of sharing real and fictional social worlds on relationship outcomes. Our findings showed that when people lack shared friends with their romantic partners, sharing media predicts greater relationship quality and people become motivated to share media with their partners. These studies show that shared media can enhance interdependence and allow people to compensate for lacking a shared social network in the real-world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.374
Teacher spread0.311 · 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 teacher head, not a consensus.

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

Citations38
Published2016
Admission routes1
Has abstractyes

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