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Record W2619568768 · doi:10.15173/glj.v8i2.3040

Where Lean May Shake: Challenges to Casualisation in the Indian Auto Industry

2017· article· en· W2619568768 on OpenAlexfundvenueno aff
Lorenza Monaco

Bibliographic record

VenueGlobal Labour Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
FundersGyeongsang National UniversityInstitute for Catastrophic Loss Reduction
KeywordsPoliticsSustainabilityMeaning (existential)Resistance (ecology)Political economyProcess (computing)BusinessLean manufacturingEconomic systemLaw and economicsSociologyPolitical scienceEconomicsLawMarketing

Abstract

fetched live from OpenAlex

By analysing the industrial conflict that has affected the Indian Maruti Suzuki since 2011/2012, the article reflects on the meaning of the lean manufacturing paradigm today. It explores what continues to make it dominant, and the ultimate frontiers it has reached. It argues that its global significance could not have been established without the exploitation of local labour regimes, and without stretching their competitive advantage to the detriment of workers. In particular, the desirable condition now sought at global level is the possibility of relying on regimes based on high levels of casualisation, allowing the progressive “substitution” of permanent workers. However, as the Maruti case also reveals, working-class composition and the sustainability of the local labour process can generate mechanisms and unexpected alliances that could potentially destabilise the system. Indeed, the case shows how corporate strategies intended to fragment and depoliticise labour, inbuilt into the paradigm, were directly challenged and encountered resistance. Ultimately, though, the case also shows how, without strong legal and political support, the potential of a labour movement can be suffocated by institutionalised violence. In this sense, lean reacts, and the despotic imposition of consent becomes visible as never before.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
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 head0.046
GPT teacher head0.315
Teacher spread0.269 · 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 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

Citations5
Published2017
Admission routes2
Has abstractyes

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