A Comparative Study of Maternity Leave Systems in the United States and Canada
Bibliographic record
Abstract
Since World War II, Western countries have paid more and more attention to protecting workers rights and interests. Various laws on employment security have been passed in turn, and the value of women has been highlighted. At the social level, women can be used as an effective supplement to the social labour force to ensure the continuous output of social productivity and, at the same time, effectively improve the social status of women; At the household level, in order to improve the low birth rate after World War II, effectively ensuring womens rights and interests can strengthen the role of female mothers, effectively increase fertility rates and ensure the health of newborns. With the exploration of womens value, how women should balance family and work has received widespread attention, and the implementation of maternity and various family benefits has protected women. However, as neighbours, the United States and Canada have very different attitudes regarding womens maternity benefits. This paper attempts to use case studies to explore why maternity benefits in the United States are less generous than those in Canada, which is also an economically developed country. Paid maternity leave is only partially impassable for the United States, but it has difficulties. The study shows that the US reluctance to provide paid leave at the national legal level is related to its market and family policies, ideology, company values, and popular support. It further explores possible recommendations for the reality of maternity benefits in the United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".