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Record W2971138635 · doi:10.54648/ijcl2019010

Revisiting Social Reproduction: Migrant Care Workers and Their Entitlements in Canada

2019· article· en· W2971138635 on OpenAlexaboutno aff
Supriya Routh

Bibliographic record

VenueInternational Journal of Comparative Labour Law and Industrial Relations · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReciprocity (cultural anthropology)CitizenshipReproductionSocial reproductionCare workSociologyPerspective (graphical)Political scienceWork (physics)LawSocial capitalSocial science

Abstract

fetched live from OpenAlex

In this article, I question the temporary/permanent divide insofar as migrant care workers’ legal entitlements are concerned with reference to the Canadian Caregiver Program, which is characterized as one of the best temporary migrant worker programs globally. I problematize the temporary/permanent distinction by critiquing the private market exchange-based relationship on the basis of which the Program is legally formulated. I argue that any caregiver program should see caregivers – irrespective of their national or foreign origin – as fully contributory members of society and thereby entitled to an extensive range of citizenship rights and entitlements. While this proposal is not completely novel, what I offer through this article is a regulatory justification for migrant caregivers’ claim to full socio-economic citizenship rights and entitlements. I develop this justification by drawing on – and reconceptualizing – the economic productivity-focused social reproduction perspective. In this article, then, I make two points: first, I propose a theoretical reconceptualization of socially reproductive work and, second, by evaluating migrant care workers’ status in Canada, I argue that on the basis of social reciprocity, caregivers cannot be considered temporary insofar as their entitlements are concerned.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.384
Teacher spread0.305 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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