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Record W2964698620 · doi:10.1177/0265407519867145

A nice surprise: Sacrifice expectations and partner appreciation in romantic relationships

2019· article· en· W2964698620 on OpenAlexaff
Giulia Zoppolat, Mariko L. Visserman, Francesca Righetti

Bibliographic record

VenueJournal of Social and Personal Relationships · 2019
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsYork UniversityUniversity of Toronto
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsSacrificeGratitudePsychologySocial psychologyRomanceSurprisePsychoanalysisTheology

Abstract

fetched live from OpenAlex

Romantic partners regularly encounter conflicts of interests and sacrifice their self-interest for their partner or the relationship. But is this relationship maintenance behavior always appreciated by the partner receiving the sacrifice? We examined whether expectations of sacrifices (i.e., beliefs that sacrifices are necessary, normal, and expected in relationships) predict people’s appreciation for their partner and, ultimately, their relationship satisfaction. Utilizing a daily experience procedure among romantic couples in the Netherlands ( N = 253 individuals), we found that when participants perceived a partner’s sacrifice, they experienced greater partner appreciation (i.e., gratitude and respect) and, in turn, felt more satisfied with their relationship when their sacrifice expectations were low, rather than high. In contrast, perceiving a partner’s sacrifice had no effect on appreciation and relationship satisfaction when the sacrifice recipient held strong sacrifice expectations. These findings illustrate the power that expectations have in influencing the receiver’s appreciation of their partner’s pro-social behavior.

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)0.015
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.055
GPT teacher head0.370
Teacher spread0.314 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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