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Record W3172016668 · doi:10.3389/fpsyg.2021.647942

Marital Surname Change and Marital Duration Among Divorcées in a Canadian County

2021· article· en· W3172016668 on OpenAlexaffabout
Melanie MacEacheron

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsWestern University
Fundersnot available
KeywordsWifePsychologyDemographyMarital statusDuration (music)SociologyPopulationPolitical science

Abstract

fetched live from OpenAlex

Women’s marital surname change has been discussed as comprising one possible signal of intention to remain married, and may be perceived as such, and valued, by husbands. Here, the practice was investigated as a potential predictor of marital duration among couples who went on to divorce. An archival analysis was based on a search of all available, opposite-sex divorces filed over an 8-month period in a Canadian county. Among couples ( n = 107) divorcing, marriages the women in which underwent marital surname change lasted 60% longer, controlling for wife’s age at the time of marriage. When the woman’s marital surname change/retention was used as a regression predictor of number of children of the marriage alongside marriage duration in years, only the latter was predictive. No husband took his wife’s surname. Giving the maternal surname (along with the paternal surname) to children occurred at a negligible frequency. Potential reasons for these findings including costly signaling and, ultimately, paternity uncertainty, as well as possible implications for public policy, are discussed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.043
GPT teacher head0.355
Teacher spread0.312 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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