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Record W4296705318 · doi:10.1111/pere.12452

Taking stock of the longitudinal study of romantic couple relationships: The last 20 years

2022· article· en· W4296705318 on OpenAlexaff
Adam M. Galovan, Terri L. Orbuch, M. Rosie Shrout, Emma Drebit, TeKisha M. Rice

Bibliographic record

VenuePersonal Relationships · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyRomancePerspective (graphical)DisadvantageDiversity (politics)Social psychologyLongitudinal studySociology

Abstract

fetched live from OpenAlex

Abstract Longitudinal dyadic research provides significant benefits for our understanding of romantic couple relationships. In this systematic review, we begin by providing a broad overview of topical trends and approaches in longitudinal couple relationships research from 2002 through 2021. Then, we narrow our review to dyadic relationship quality articles, highlighting key themes as well as noting important gaps in the research. Using an intersectional perspective that acknowledges multiple ways that disadvantage, power, and oppression may be seen in both research and in couples' lived experience, we note prominent paradigms used in examining couple relationships, what types of questions have been most valued, and what groups and approaches are underrepresented in the literature. Most longitudinal couple relationships research is quantitative, relies on self‐report approaches from American couples in the early‐to‐middle years of their relationships, concentrates more on negative aspects of relationships than positives, and takes a communication‐satisfaction paradigm in studying couples. We see a clear need to increase the use of methodologies beyond self‐report measures, conduct more studies with within‐group minority, older adult, culturally‐diverse, and context‐specific samples to explore the diversity of relationships, and fully consider both strengths and positive processes in relationships as well as the challenges couples experience.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.380
Teacher spread0.261 · 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.

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

Citations22
Published2022
Admission routes1
Has abstractyes

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