MétaCan
Menu
Back to cohort
Record W3049088103 · doi:10.3138/cpp.2020-091

Reconceptualizing Parental Leave Benefits in COVID-19 Canada: From Employment Policy to Care and Social Protection Policy

2020· article· en· W3049088103 on OpenAlexaffvenueabout
Andrea Doucet, Sophie Mathieu, Lindsey McKay

Bibliographic record

VenueCanadian Public Policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsThompson Rivers UniversityUniversité TÉLUQCarleton UniversityBrock University
FundersLeibniz-GemeinschaftGottfried Wilhelm Leibniz Universität HannoverNational Science Foundation
KeywordsParental leaveSocial policyCitizenshipPolitical sciencePandemicCoronavirus disease 2019 (COVID-19)Public policyPoliticsEconomic growthSociologyEconomicsDiseaseMedicineLaw

Abstract

fetched live from OpenAlex

Although the coronavirus disease 2019 (COVID-19) pandemic has spurred critical and much-needed attention to re-thinking policy approaches to child care and long-term elder care, little focus has been given to its implications for parental leave policies and parental benefits for the care of infants and young children. This article is about reconceptualizing and reconfiguring employment-based parental leave policies in Canada both during and after COVID-19. Informed by theoretical insights from the fields of care economies, feminist political economy, and care and social reproduction and by national and international parental leave research, we argue that it is time to reconceptualize parental leave benefits not only as employment policy but also as a care and social protection policy. To make this shift, we explore three topic areas: a mixed system of parental benefits that combine employment-based and citizenship-based entitlements, connections between policy design and gender equality, and the need for robust intersectional data to track which Canadian families are receiving parental benefits.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.127
GPT teacher head0.382
Teacher spread0.255 · 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 designNot applicable
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

Citations24
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Public PolicySame topicEmployment and Welfare StudiesFrench-language works237,207