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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
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