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Record W2345430417 · doi:10.1177/0022185616643558

Parental-leave rich and parental-leave poor: Inequality in Canadian labour market based leave policies

2016· article· en· W2345430417 on OpenAlexaffabout
Lindsey McKay, Sophie Mathieu, Andrea Doucet

Bibliographic record

VenueJournal of Industrial Relations · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité de MontréalBrock University
Fundersnot available
KeywordsParental leaveScrutinyInequalityDemographic economicsSocial inequalityLabour economicsRest (music)ReproductionPolitical scienceEconomicsWork (physics)MedicineLaw

Abstract

fetched live from OpenAlex

Canada has two parental leave benefit programs for the care of a newborn or adopted child: a federal program, and, since 2006, a provincial program in Québec. Informed by a social reproduction framework, this article compares access to parental leave benefits between Québec and the rest of Canada by family income and by its two different programs. Our analysis of quantitative data reveals that maternal access to leave benefits has improved dramatically over the past decade in the province of Québec, especially for low-income households. By contrast, on average 38% of mothers in the rest of Canada are consistently excluded from maternity or parental benefits under the federal program. We argue that one key explanation for the gap in rates of access to benefits between the two programs and between families by income is difference in eligibility criteria. In Canada, parental leaves paid for by all employers and employees are unevenly supporting the social reproduction of higher earners. Our article draws attention to the need for greater public and scholarly scrutiny of social class inequality effects of parental leave policy.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.383
Teacher spread0.293 · 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.

Study designObservational
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

Citations77
Published2016
Admission routes2
Has abstractyes

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