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Record W2407992205 · doi:10.1177/0022185616648487

Cash for care in Quebec, collective labour rights and gendered devaluation of work

2016· article· en· W2407992205 on OpenAlexafffundabout
Louise Boivin

Bibliographic record

VenueJournal of Industrial Relations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité du Québec en Outaouais
FundersMinistère de la Santé et des Services sociaux
KeywordsDevaluationLabour lawCashCare workLabor relationsWork (physics)Norm (philosophy)Labour economicsSocial securityBusinessEconomicsPolitical scienceLawFinanceMarket economy

Abstract

fetched live from OpenAlex

Work performed under cash-for-care programmes is based on a relationship between several parties, including, at a minimum, the workers providing the services, the care recipients and the public authorities that manage and fund these programmes. Labour law studies have pointed out that the labour relations regulation is not adapted to this type of non-standard employment relationship since it has been founded on the norm of the integrated firm and bilateral employer–employee relations. Based on a case study of a cash-for-care programme in Quebec, Canada (i.e. the Service Employment Paycheque plan), our socio-legal analysis confirms the weak protection of collective labour rights provided to Service Employment Paycheque plan workers. It also describes how the application of the legal regulation of labour relations to this organizational model fails to take into account the power exercised by the public authorities and demonstrates the impact of this failure in terms of precarization of work and its gendered devaluation.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.079
Threshold uncertainty score0.575

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.015
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.086
GPT teacher head0.350
Teacher spread0.265 · 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
Published2016
Admission routes3
Has abstractyes

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