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Record W2461222602 · doi:10.4000/activites.573

Développer le travail d’organisation pour transformer l’organisation du travail

2013· article· fr· W2461222602 on OpenAlexaff
Sébastien Bruère, Jérôme Chardeyron

Bibliographic record

VenueActivites · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité Laval
FundersNational Institute for Occupational Safety and Health
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article présente une méthodologie d’intervention visant à agir sur l’organisation du travail dans un contexte de système de production lean manufacturing. Pour répondre aux demandes de plusieurs entreprises industrielles connaissant des problèmes de troubles musculosquelettiques et de stress, pendant ou à l’issue de l’implantation d’une organisation lean, nous avons dû concevoir une nouvelle méthodologie d’intervention. Notre cadre théorique fera appel aux concepts de travail d’organisation et de développement de l’activité. Ces dernières nous ont permis d’identifier des dispositifs organisationnels du lean qui, dans les situations de travail, ont des effets paradoxaux sur la santé. Ces revues nous ont également donné la possibilité d’aborder les mécanismes qui conduisent à l’apparition de ces paradoxes. La dimension d’amélioration continue du lean manufacturing et cette ambivalence de certains dispositifs nous a conduits à nous poser des questions sur les actes qui construisent cette organisation, c’est-à-dire au travail d’organisation. Après avoir présenté cette méthodologie d’intervention pour développer le travail d’organisation, nous verrons que de nouvelles questions de recherche apparaissent, ouvrant la voie à de futures recherches-interventions.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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