Employment Rates of Women with Young Children in Canada
Bibliographic record
Abstract
I would like to thank the Canadian International Labour Network for financial support. I would also like to thank Stephen Jones, Peter Kuhn, Lonnie Magee and Martin Dooley of McMaster University for their guidance and extensive comments. Thanks are also due to Eden Thompson of the Applied Research Branch at Human Resources Development Canada for her comments. All remaining errors 2 Maternity and parental leave policies are on the forefront of the current political agenda in Canada. This paper answers the question: does maternity and parental leave (M/PL) policy raise or lower the probability of employment for women? One unique feature of M/PL policy in Canada is the variation in mandated unpaid job-protected leave allowances across provinces. This variation is used in this study to identify the effect of provincial M/PL policies on employment rates of women with young children. Using the Canadian Labour Force Survey (LFS) data from 1976 to 2000, I find evidence that M/PL policy reduces the gap between the employment probabilities of women with young children versus women with older children. Moreover, a difference-in-differences model predicts a 3 to 4 percent increase in the probability of employment for women with young children (aged 0 to 2) relative to
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".