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Record W4414647475 · doi:10.3138/cjhs-2025-0002

Monogamy in question: Predictors of perceived shifts in attitudes toward monogamy and consensual non-monogamy during the COVID-19 pandemic

2025· article· en· W4414647475 on OpenAlexvenueno aff
Abigail Masterson, Elizabeth A. Mahar, Amy C. Moors, Jessica T. Campbell, Nina Dours, Amanda N. Gesselman

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsOpenness to experienceSocioemotional selectivity theoryPandemicRomanceSexual behaviorHuman factors and ergonomicsPoison controlBiological sex

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has been linked to increased self-reflection and shifts in personal priorities, including romantic and sexual relationships. Drawing on socioemotional selectivity theory and terror management theory, this study examines whether the pandemic influenced U.S. adults’ attitudes toward monogamy and openness to consensual non-monogamy (CNM). Using a national sample of 2,004 adults surveyed in January 2021, the authors explored the participants’ perceived changes in the importance of monogamy and the likelihood of pursuing CNM relationships, along with demographic correlates. The results showed that for most participants, monogamy’s importance to them remained stable, yet 23% reported decreased importance, and 11% expressed greater interest in CNM relationships. Demographic factors, such as age, transgender identity, sexual orientation, and parental status, were significantly associated with attitudes toward monogamy and CNM. These findings highlight the pandemic’s dual role in reinforcing monogamy for some while prompting others to explore alternative relationship structures. This study underscores the importance of understanding relational shifts during societal disruptions and offers a foundation for future research on how crises shape intimate relationships.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.671
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 head0.093
GPT teacher head0.392
Teacher spread0.299 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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