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Record W2889811678 · doi:10.1037/pspi0000147

Can you get the magic back? The moderating effect of passion decay beliefs on relationship commitment.

2018· article· en· W2889811678 on OpenAlexfundno aff
Kathleen L. Carswell, Eli J. Finkel

Bibliographic record

VenueJournal of Personality and Social Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNorthwestern University
KeywordsPassionPsychologyRomanceModerationSocial psychologyPsycINFOConstruct (python library)Set (abstract data type)MAGIC (telescope)PsychoanalysisChemistry

Abstract

fetched live from OpenAlex

The present research introduces the construct of a decay theory of passion-a lay belief that romantic passion decline is irreversible-and investigates how this construct interacts with existing levels of passion for one's romantic partner to predict lower relationship commitment and greater pursuit of romantic alternatives. Across three studies employing experimental and nonexperimental procedures-and a set of meta-analytic syntheses including additional studies-results generally supported the hypotheses that, although low passion is linked to lower commitment and greater pursuit of romantic alternatives, such effects are stronger when adherence to decay beliefs is high rather than low. These effects tended to be independent of effects of destiny and growth theories (Knee, 1998), a related set of lay theories in the domain of relationships. Mediated moderation analyses revealed that the moderating effect of decay theories on relationship commitment mediates the moderating effect of decay theories on the link between low passion and the pursuit of romantic alternatives. Discussion addresses the possibility that changing one's beliefs surrounding the nature of romantic passion may be an important, but previously overlooked, means for preventing one from prematurely abandoning an otherwise satisfying relationship. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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)0.002
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 score0.245
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.057
GPT teacher head0.428
Teacher spread0.370 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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