Research on Parental Leave Policies and Children's Development Implications for Policy Makers and Service Providers
Bibliographic record
Abstract
Introduction and Subject Relevance Maternity and parental leave policies date back more than 100 years and are now established policy instruments in over 120 nations. Typically, national policies include a period of job-protected leave (averaging 44 weeks across OECD countries) and some degree of income replacement (benefits) in order to enable mothers and (increasingly) fathers to take a period of time off of work following the birth or adoption of a child. Parental leaves and benefits are variously referred to as family policies that protect maternal and infant health; as employment policies that promote gender equity and respect the rights of workers to combine work and family responsibilities; and as “an essential ingredient in early childhood education and care policies.” Current trends include extending the period of available leave (as per recent changes in Canada, where eligible parents can share up to a full year of maternity and parental leave benefits), promoting paternal leave, and adding more flexible options. Until recently, much of the research in this area has focussed primarily on use patterns and the economic consequences of leave policies. However, there is now considerable interest in the effects of leave policies and leave duration on mothers’ physical and mental health and on children’s development.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".