Defending Informal Workers’ Welfare Rights: Trade Union Struggles in Tamil Nadu
Bibliographic record
Abstract
The South Indian state of Tamil Nadu has had a rich history of informal workers’ movements and struggles that have pressured the state government to enact statutory schemes and set up worker welfare boards to extend social protection to informal workers. This article discusses the efforts of two prominent trade unions in the state to secure welfare benefits for informal workers, and explores the primary challenges, conflicts and dilemmas they have faced. It explores the troubled interfaces between trade unions and the worker welfare boards that the unions regard as the fruit of workers’ struggles and collective organising of the past. The unions have used the welfare boards to mobilise new occupational categories of workers as well as women workers in the lower rungs of the informal sector. At the same time, the welfare boards are a double-edged sword that the unions must carefully manage given the frustration and disappointments that ensue when the promise of social protection remains elusive to workers. Placing this case study in the larger context of labour movements across the world that have won contingent victories in protecting workers’ interests and well-being, the article raises troubling questions regarding the implications of these victories in neo-liberal state regimes. KEY WORDS: informal workers; trade unions; welfare rights; labour organising; Tamil Nadu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".