Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".