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Record W4403099822 · doi:10.1177/07308884241288580

Are New Technologies Empowering Workers? Digital Lean Production and the Reorganization of Work in Manufacturing

2024· article· en· W4403099822 on OpenAlexafffund
Mathieu Dupuis, Alexis Massicotte

Bibliographic record

VenueWork and Occupations · 2024
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsLean manufacturingWork (physics)Production (economics)BusinessManufacturing engineeringOperations managementLabour economicsEngineeringMarketingEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

While Lean production is the dominant productive system in manufacturing, recent debates over digitalization have given rise to predictions of a new wave of work reorganization with potential benefits for workers. To what extent are Lean production and digitalization making headway in corporations and workplaces, and are they doing so in tandem? The present article argues that contradictions between distinct organizational levels follow the deployment of ‘Digital Lean’ practices. At the corporate level, these principles have spread within firms and managerial beliefs, yet their integration within workplaces has been far from unilateral. An analysis of the aluminum and rubber manufacturing sectors identifies two models of work organization, Empowered Digital Lean and Taylorized Digital Lean systems. The study shows that differences between the two regimes result from differing product markets, production characteristics and levels of workers’ power, while highlighting potential points of resistance for labor.

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)0.003
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.017
Scholarly communication0.0070.007
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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