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Record W2522475109 · doi:10.54648/ijcl2016016

Just-in-Time Labour: The Case of Networks Providing Home Support Services in Quebec

2016· article· en· W2522475109 on OpenAlexaboutno aff
Louise Boivin

Bibliographic record

VenueInternational Journal of Comparative Labour Law and Industrial Relations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsObligationWorking timeContext (archaeology)Work (physics)Compensation (psychology)Labour economicsDivision of labourLabour lawBusinessPublic relationsEconomicsLawPolitical scienceSocial psychologyPsychologyEngineering

Abstract

fetched live from OpenAlex

This article aims to analyse how the obligation to be available to work when requested by one’s employer manifests itself in the context of network organizations and under a legal regulation of working hours which was forged on the basis of the industrial production model. We conducted two case studies examining the situation of workers in the private externalized segments of home support services networks in Quebec, a Canadian province. We will demonstrate that for these atypical workers, mostly women, the obligation to be available to work without any accompanying compensation in wages is implicit and manifests itself in two ways: through time that is considered to be personal but which is not because it is squeezed into the gaps in fragmented, variable and unpredictable working schedules and through the exclusion, from paid work time, of time spent carrying out work-related tasks. The impact of this obligation is significant in terms of wages and precariousness. Its manifestations derive from both the network-based organization of work – which allows for the imposition of a ‘justin- time’ model of labour management and a reinforcement of the sexual division of labour – and the inadequacy of the legal regulation of working hours with regard to this organizational model.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.034
GPT teacher head0.307
Teacher spread0.274 · 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 designTheoretical or conceptual
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

Citations8
Published2016
Admission routes1
Has abstractyes

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