Bibliographic record
Abstract
The struggle to balance work responsibilities with family obligations may be most difficult for working parents of the youngest children, those five and under. Any policy changes designed to ease the difficulties for these families are likely to be controversial, requiring a careful effort to weigh both the costs and benefits of possible interventions while respecting diverse and at times conflicting American values. In this article, Christopher Ruhm looks at two potential interventions-parental leave and early childhood education and care (ECEC)-comparing differences in policies in the United States, Canada, and several European nations and assessing their consequences for important parent and child outcomes. By and large, Canadian and European policies are more generous than those in the United States, with most women eligible for paid maternity leave, which in a few countries can last for three years or more. Many of these countries also provide for paid leave that can be used by either the mother or the father. And in many European countries ECEC programs are nearly universal after the child reaches a certain age. In the United States, parental leave, if it is available, is usually short and unpaid, and ECEC is generally regarded as a private responsibility of parents, although some federal programs help defray costs of care and preschool education. Ruhm notes that research on the effects of differences in policies is not completely conclusive, in part because of the difficulty of isolating consequences of leave and ECEC policies from other influences on employment and children's outcomes. But, he says, the comparative evidence does suggest desirable directions for future policy in the United States. Policies establishing rights to short parental leaves increase time at home with infants and slightly improve the job continuity of mothers, with small, but positive, long-run consequences for mothers and children. Therefore, Ruhm indicates that moderate extensions of existing U.S. leave entitlements (up to several months in duration) make sense. He also suggests that some form of paid leave would facilitate its use, particularly among less advantaged parents, and that efforts to improve the quality of ECEC, while maintaining or enhancing affordability, are desirable.
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 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".