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Record W4390585882 · doi:10.1177/02610183231219187

Bordering social reproduction: The welfare/immigration regimes of Quebec and Ontario in Canada

2024· article· en· W4390585882 on OpenAlexafffundabout
Karine Côté-Boucher, Susan Braedley

Bibliographic record

VenueCritical Social Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsCarleton UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWelfare stateSocial policyImmigrationSocial reproductionWelfareReproductionCitizenshipSocial WelfareSociologyPolitical scienceDemographic economicsEconomicsSocial scienceLawSocial capitalBiologyEcology

Abstract

fetched live from OpenAlex

This article makes three crucial, related arguments. First, most European analyses of immigration and social welfare fail to consider how these policies intersect to shape the social reproduction of populations, instead sticking to notions of welfare chauvinism, social citizenship, and deservingness. Second, welfare/immigration analyses are usually set at the national level, but subnational comparisons can challenge tidy welfare state regime categorizations, revealing both nuance and policy opportunities. Third, a focus on social reproduction regimes that includes welfare and immigration policies reveals how jurisdictions border the extraction of social reproductive labour, with impacts on who gets in and under which conditions, and on the distribution of paid and unpaid social reproductive work within immigrant and established families in Canada. Developing our feminist border analysis, we illustrate our approach through a comparative analysis of Quebec and Ontario to show how social reproductive borders extract care labour and from whom, under diverging policy regimes.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.222
Threshold uncertainty score0.903

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0210.008
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.338
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes3
Has abstractyes

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