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The Role of the State towards the Grey Zone of Employment: Eyes on Canada and the United States

2017· article· fr· W2614467924 on OpenAlexaffvenueabout
Susan Bisom–Rapp, Urwana Coiquaud

Bibliographic record

VenueInterventions économiques · 2017
Typearticle
Languagefr
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Dans la plupart des pays, le travail précaire est en hausse et les formes atypiques de travail se multiplient. Ce que nous appelons la « zone grise » de l’emploi résulte tant des transformations du travail typique que du travail atypique. En mettant l’accent sur la réglementation, les politiques publiques et le rôle de l’État dans la création et la perception de la zone grise, notre contribution consiste à relever les agissements ou les défauts d’agissements du gouvernement et les conséquences induites sur la relation d’emploi typique. En examinant et en comparant les conditions dans nos deux pays, le Canada et les États-Unis, nous montrons que l’État joue un rôle paradoxal dans la croissance du travail atypique et du travail précaire. Pour appuyer notre analyse, nous avons développé une matrice pour saisir les efforts ou les inerties du gouvernement. Nous concluons qu’il y a sept façons de comprendre le rôle joué par le gouvernement à l’égard de la zone grise.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.422
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.062
GPT teacher head0.361
Teacher spread0.300 · 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.

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

Citations11
Published2017
Admission routes3
Has abstractyes

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