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Record W4319845316 · doi:10.1177/0192513x231151297

Trajectories of Paternal Contact After Parental Separation: A Latent Class Growth Analysis

2023· article· en· W4319845316 on OpenAlexafffundabout
Amandine Baude, Sylvie Drapeau, Véronique Lachance, Hans Ivers

Bibliographic record

VenueJournal of Family Issues · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentres Intégré Universitaires de Santé et de Services SociauxCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité LavalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSeparation (statistics)Latent class modelSocioeconomic statusDevelopmental psychologyPsychologyDemographyLatent growth modelingStatisticsMathematicsSociologyPopulation

Abstract

fetched live from OpenAlex

A latent class growth curve analysis was conducted to model post-separation, paternal involvement trajectories over an 8-year period from the time of separation. Our sample was composed of 245 separated mothers whose data was drawn from a representative survey of Québec children. Four classes of distinct trajectories were identified: shared-time, near-shared-time, moderate-stable, and low-decreasing. The majority of fathers maintained a consistently high level of contact with their children, and the frequency of visits at the time of separation seemed to determine future father–child contact. Several factors predicted contact trajectories, including socioeconomic status, a new partner in the mother’s life, and the quality of the inter-parental relationship.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.173
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.352
Teacher spread0.311 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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