Organizing in the informal sector : A case study in Mumbai's shipbreaking yards
Bibliographic record
Abstract
ABSTRACTBuilding on empirical data collected through interviews with representatives of the Mumbai Port Trust Dock and General Employees Union, the International Metalworkers’ Federation, and shipbreaking workers, this paper presents the results of a case study conducted from 2011 to 2013 in the shipbreaking yards of Mumbai. I first examine India’s liberalization shift in the early 1990s and observe its effect on the transformation of labour markets, then present a brief overview of the literature related to unions and the informal economy. Using the conceptual framework developed by Sousa Santos (2004) around the “sociology of absences, the sociology of emergences and the work of translation” and the analytical tool developed by Comeau (2005) to study collective struggles, the core of the article explores the development of shipbreaking activities in India, chronicles the history of collective action in the shipbreaking industry, discusses practices, strategies and demands put forward by the unions, and ...
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".