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Record W4384200294 · doi:10.32920/23589618.v1

Re-assessing caregiver migration programs and policies in Canada

2023· preprint· en· W4384200294 on OpenAlexaffabout
Isabella Eldeib

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsGovernment (linguistics)ConversationPolitical scienceRacismInequalityPandemicOrder (exchange)Face (sociological concept)Work (physics)Care workPublic policyEconomic growthCoronavirus disease 2019 (COVID-19)SociologyPsychologyGender studiesMedicineBusinessEconomicsLaw

Abstract

fetched live from OpenAlex

Caregiver migration programs and policies in Canada have  undergone numerous changes since the implementation of the Live-in Caregiver Program (LCP) in 1992. Although changes made by the Canadian government between 1992 and 2020 claimed to “support†migrant caregivers, many caregivers continue to face precarious conditions. The purpose of this Major Research Paper is to argue that caregiver migration programs and policies need to be reassessed and challenged as they continue to embody problematic labour practices that render migrant caregivers vulnerable. Broadly, this research brings literature on racism, sexism and colonialism into one conversation in order to better understand the root causes of inequality faced by migrant caregivers in Canada. The significance of this work lies in its provision of a contemporary understanding of caregiver migration, especially in light of a global pandemic, in order to advocate for policy amendments that will genuinely support migrant caregivers and lead to the elimination of exploitative care labour practices. Key Words: Canadian Caregiver Migration Policies, Caregiver Migration, Decolonial Lens, COVID-19, Migrant Care Workers, Migrant Caregivers, Care Work.

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)0.005
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.852

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0120.002
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.326
Teacher spread0.281 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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