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Record W3192278709 · doi:10.1111/ajsp.12497

Who moved with you? The companionship of significant others reduces movers’ motivation to make new friends

2021· article· en· W3192278709 on OpenAlexaff
Wen‐Qiao Li, Liman Man Wai Li, Nigel Mantou Lou

Bibliographic record

VenueAsian Journal Of Social Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsInterpersonal relationshipPsychologyAffect (linguistics)Social psychologyInterpersonal communicationFriendshipSocial relationDevelopmental psychologyCommunication

Abstract

fetched live from OpenAlex

This research investigated how residential moves with versus without the companionship of significant others would affect people’s motivation to make new friends. Studies 1a and 1b showed that the companionship of significant others predicted fewer new friends among university students who moved within the same country (Study 1a) and to a different country (Study 1b), suggesting that the companionship of significant others was associated with a lower level of motivation to make new friends. In Study 2, the results of an experiment demonstrated that the companionship of a significant other reduced movers’ motivation to make new friends, and this was explained by positive affect but not negative affect. Specifically, the companionship of a significant other, compared with the companionship of an acquaintance or no companionship, led to stronger positive affect, which, in turn, reduced motivation to make new friends. Taken together, these findings call for more nuanced theory on the influence of residential mobility on well‐being and social networks.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.350
Teacher spread0.299 · 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.

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

Citations8
Published2021
Admission routes1
Has abstractyes

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