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Record W2605381450 · doi:10.25336/p6nc8q

Non-residential fatherhood in Canada

2017· article· en· W2605381450 on OpenAlexafffundvenueabout
Lisa Strohschein

Bibliographic record

VenueCanadian Studies in Population · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDemographyHumanitiesMarital statusGeographyLogistic regressionSociologyPolitical sciencePopulationArtMathematicsStatistics

Abstract

fetched live from OpenAlex

The purpose of this study was to shed light on non-residential fatherhood in Canada. Data come from the 2012 LISA. Analysis was restricted to fathers who had children under the age of 19 (N=3,592). Approximately 17.4% were non-residential fathers. Logistic regression models indicated that being outside a marital union, low educational attainment and low income were associated with increased odds of being a non-residential father. Teen parenthood was not a statistically significant predictor. I discuss the implications of these findings as well as the need for measures that better capture variability in the living arrangements of fathers and their children.Le but de cette étude est d’éclairer le phénomène de paternité non résidentielle au Canada. Les données proviennent du sondage LISA 2012. L'analyse est limitée aux pères ayant des enfants de moins de 19 ans (N = 3 592). Environ 17,4% sont des pères non-résidentiels. Les modèles de régression logistique indiquent qu'étant hors d'une union maritale, d'avoir un faible niveau de scolarité, et de faible revenu est associé à une probabilité élevée d'être un père non-résidentiel. Être un parent adolescent n’est pas un prédicteur statistiquement significatif. Je discute des implications de ces résultats ainsi que de la nécessité de mesures qui permettent de mieux saisir la variabilité des modes de vie des pères et de leurs enfants.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.352
Teacher spread0.285 · 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

Citations1
Published2017
Admission routes4
Has abstractyes

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