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Record W7007843789

Analyse sociohistorique des transformations du régime assurantiel québécois en matière de santé et sécurité au travail (1885-2015)

2017· other· fr· W7007843789 on OpenAlexaboutno aff

Bibliographic record

VenueArchipelago (Université du Québec à Montréal) · 2017
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrial relationsContext (archaeology)Trade unionCommission
DOInot available

Abstract

fetched live from OpenAlex

La Commission de la santé et de la sécurité du travail (CSST) a administré le régime québécois de santé et sécurité du travail de 1985 à 2015. Elle a remplacé la Commission des accidents du travail (CAT) qui a géré le régime de 1931 à 1979. L'objet de ce mémoire est de comprendre l'évolution historique du programme assurantiel québécois en matière de santé et sécurité du travail. Nous nous pencherons particulièrement sur la réforme de l'indemnisation de la CSST qui a lieu en 1985. Le cadre théorique utilisé s'inspire des travaux de l'approche institutionnelle de Robert J. Commons, de l'école de la régulation et de l'approche théorique sur le néolibéralisme de Loïc Wacquant et de Sylvie Morel. Dès le développement de l'économie moderne au début du XIXe siècle, les questions liées à la santé et sécurité du travail sont au cœur des tensions et des revendications prenant place entre le salariat et le patronat. L'évolution des rapports de force socioéconomiques a un impact majeur sur la régulation de la santé et sécurité du travail. Ce domaine fondamental des relations de travail s'est transformé avec l'économie du Québec. \n______________________________________________________________________________ \nMOTS-CLÉS DE L’AUTEUR : santé et sécurité du travail, régime assurantiel, fordisme, CSST, CAT, workfare, néolibéralisme, salaire indirect

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)0.001
metaresearch head score (Gemma)0.001
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 score0.963
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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
Published2017
Admission routes1
Has abstractyes

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Same venueArchipelago (Université du Québec à Montréal)French-language works237,207