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A Comparative Study of Maternity Leave Systems in the United States and Canada

2023· article· en· W4388814104 on OpenAlexaboutno aff
Jie Feng

Bibliographic record

VenueCommunications in Humanities Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsProductivitySocial securityEconomic growthPopulationBirth ratePolitical scienceValue (mathematics)IdeologyTotal fertility rateFamily planningFertilityBusinessMedicineEconomicsLawPoliticsEnvironmental health

Abstract

fetched live from OpenAlex

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 women’s rights and interests can strengthen the role of female “mothers”, effectively increase fertility rates and ensure the health of newborns. With the exploration of women’s 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 women’s 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.458
GPT teacher head0.487
Teacher spread0.028 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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