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Record W2978834795

Mise en union pr?coce au Canada: liens avec l'?ducation

2007· article· fr· W2978834795 on OpenAlexaboutno aff
Darcy Hango

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsDemographic economicsPsychologyPolitical scienceLongitudinal studySurvey data collectionLongitudinal dataHazardDemographySociologyEconomicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

This article examines the link between early union formation and edu cation using a new Canadian longitudinal data set, the Youth in Transition Survey (YITS). Educational transitions and early union formation occur around the same time in young adulthood, yet the roles of student and conjugal partner are often thought to be incompatible. We examine the effect of two important educational indicators (exit from full-time school and level of achieved education) on the timing of the first conjugal union. In addition, we incorporate several more direct measures of educational commitment. Results from proportional hazard models reveal that exit from full-time school greatly increases the transition to first union, especially marriage. Similarly, obtaining a post-secondary degree/diploma also significantly increases the risk of forming a union, especially for women. More direct measures of educational commitment show that skipping classes in high school has a negative effect on the risk of marriage, but a positive effect on cohabiting unions. A greater aspiration for future education, meanwhile, has a negative impact on union formation in general. The analysis is based on research carried out in the Quebec Inter-University Centre for Social Statistics, which provides researchers with access to detailed longitudinal survey data collected by Statistics Canada. The opinions expressed here do not represent the views of Statistics Canada.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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 designTheoretical or conceptual
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
Published2007
Admission routes1
Has abstractyes

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