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Record W2800050806 · doi:10.1111/pere.12238

Can you make my goals easier to achieve? Effects of partner instrumentality on goal pursuit and relationship satisfaction

2018· article· en· W2800050806 on OpenAlexfundno aff
Angela C. Cappuzzello, Judith Gere

Bibliographic record

VenuePersonal Relationships · 2018
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyGoal pursuitSocial psychologyRomanceMultilevel modelPartner effectsDevelopmental psychology

Abstract

fetched live from OpenAlex

Goal pursuits are strongly influenced by romantic partners, and a partner's instrumentality to goals may be particularly important for goal pursuit. This study examined the effects of partner instrumentality on goal‐related effort, goal progress, goal commitment, and relationship satisfaction over time. It also examined whether relationship satisfaction moderated the effects of partner instrumentality on goal pursuit processes. Newly dating romantic partners (N = 59 couples) reported on their goals and relationship satisfaction at two assessments 3 months apart. Multilevel models indicated that partner instrumentality predicted increases in progress over time but only for those high in relationship satisfaction. Partner instrumentality also predicted increases in one's own relationship satisfaction but was unrelated to changes in the partner's satisfaction. These findings suggest that partner instrumentality benefits increased goal progress, particularly for those with satisfying relationships, and further increases relationship satisfaction.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.041
GPT teacher head0.367
Teacher spread0.326 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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