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Record W3136390851 · doi:10.1177/02654075211000436

Planning date nights that promote closeness: The roles of relationship goals and self-expansion

2021· article· en· W3136390851 on OpenAlexafffund
Cheryl Harasymchuk, Deanna L. Walker, Amy Muise, Emily A. Impett

Bibliographic record

VenueJournal of Social and Personal Relationships · 2021
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsWestern UniversityUniversity of TorontoYork UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClosenessPsychologySocial psychologyRomanceDevelopmental psychologyMathematicsPsychoanalysis

Abstract

fetched live from OpenAlex

Spending time with a romantic partner by going on dates is important for promoting closeness in established relationships; however, not all date nights are created equally, and some people might be more adept at planning dates that promote closeness. Drawing from the self-expansion model and relationship goals literature, we predicted that people higher (vs. lower) in approach relationship goals would be more likely to plan dates that are more exciting and, in turn, experience more self-expansion from the date and increased closeness with the partner. In Study 1, people in intimate relationships planned a date to initiate with their partners and forecasted the expected level of self-expansion and closeness from engaging in the date. In Study 2, a similar design was employed, but we also followed up with participants 1 week later to ask about the experience of engaging in their planned dates (e.g., self-expansion, closeness from the date). Taken together, the results suggest that people with higher (vs. lower) approach relationship goals derive more closeness from their dates, in part, because of their greater aptitude for planning dates that are more exciting and promote self-expansion.

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 score0.094
Threshold uncertainty score0.646

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.380
Teacher spread0.301 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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