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Record W3194039109 · doi:10.3138/cjhs.2021-0011

Unspoken, yet understood: Exploring how couples communicate their exclusivity agreements

2021· article· en· W3194039109 on OpenAlexaffvenue
Megan D. Muise, Charlene F. Belu, Lucia F. O’Sullivan

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNegotiationRomanceMeaning (existential)Social psychologyPsychologyConversationValue (mathematics)Political scienceCommunicationLawComputer science

Abstract

fetched live from OpenAlex

Although monogamy (i.e., romantic and/or sexual exclusivity) remains the most common arrangement for romantic partnerships, there is little research exploring how couples communicate about exclusivity to one another. The current study assessed the ways in which couples discuss and negotiate exclusivity agreements, and whether those agreements change over time. Participants were 573 North American adults (mean age = 28.86 years; 52% identified as female) in romantic relationships who completed an online survey asking them to describe their current exclusivity agreements using both structured and open-ended survey questions. Open-ended data were subjected to inductive content analysis, and eight primary themes were identified. Although most (91%) indicated that they have an agreement to remain romantically and sexually exclusive in their relationships, only 43% reported coming to the agreement during an explicit conversation with their partner. More often (52%) the agreements were described as implied, meaning they had never actually been discussed. Of those with exclusivity agreements, 87% reported no change to their agreement throughout the relationship. Implications are discussed in terms of the value of direct communication between partners about exclusivity and infidelity.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.401
GPT teacher head0.370
Teacher spread0.031 · 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 designQualitative
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

Citations9
Published2021
Admission routes2
Has abstractyes

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