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Record W4285077203 · doi:10.1177/09567976221081872

Feeling Appreciated Buffers Against the Negative Effects of Unequal Division of Household Labor on Relationship Satisfaction

2022· article· en· W4285077203 on OpenAlexafffund
Amie M. Gordon, Emily S. Cross, Esra Ascigil, Rhonda Nicole Balzarini, Anna Luerssen, Amy Muise

Bibliographic record

VenuePsychological Science · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingPsychologySocial psychologyDivision of labourSocioeconomic statusDevelopmental psychologyGlobePandemicCoronavirus disease 2019 (COVID-19)Demographic economicsDemographyEconomicsSociology

Abstract

fetched live from OpenAlex

Decades of research from across the globe highlight unequal and unfair division of household labor as a key factor that leads to relationship distress and demise. But does it have to? Testing a priori predictions across three samples of individuals cohabiting with a romantic partner during the COVID-19 pandemic ( N = 2,193, including 476 couples), we found an important exception to this rule. People who reported doing more of the household labor and who perceived the division as more unfair were less satisfied across the early weeks and ensuing months of the pandemic, but these negative effects disappeared when people felt appreciated by their partners. Feeling appreciated also appeared to buffer against the negative effects of doing less, suggesting that feeling appreciated may offset the relational costs of unequal division of labor, regardless of who contributes more. These findings generalized across gender, employment status, age, socioeconomic status, and relationship length.

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)0.001
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 score0.114
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.049
GPT teacher head0.394
Teacher spread0.346 · 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

Citations33
Published2022
Admission routes2
Has abstractyes

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