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Record W2980134294 · doi:10.15173/glj.v10i3.3706

Defending Informal Workers’ Welfare Rights: Trade Union Struggles in Tamil Nadu

2019· article· en· W2980134294 on OpenAlexvenueno aff
K. Kalpana

Bibliographic record

VenueGlobal Labour Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsInformal sectorTamilTrade unionWelfareCasteState (computer science)Context (archaeology)Statutory lawGovernment (linguistics)Welfare statePolitical scienceLabour economicsPolitical economyEconomic growthSociologyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

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

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.262
Teacher spread0.255 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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