Where Lean May Shake: Challenges to Casualisation in the Indian Auto Industry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".