Labour institutions and the dynamic production of informality: collective organisation of hard-to-reach workers in Tanzania
Bibliographic record
Abstract
This paper discusses the role of labour regulation and trade unions in collective organisation of workers in non-standard, diffuse and informal labour relations in the Global South. The central argument is that labour institutions interlink with and co-create different configurations of informality and hence possibilities for collective organisation. This argument responds to calls in global labour studies for new conceptions of labour struggles that go beyond Eurocentrism and a narrow focus on traditional tools and institutions of workers’ power in the global context. Challenging the formal-informal dualism, the empirical material presented in this paper suggests a more nuanced understanding of the role of labour regulation and trade unions as sites for both the production and the contestation of the category of informal work. This is illustrated by efforts for collective organisation of hard-to-reach workers in the two dissimilar sectors of street vending and domestic work in Tanzania. Using the power resources approach as a conceptual framework for structuring the analysis, the paper examines how collective organisation interlinks dynamically with specific configurations of labour informality which derive from the labour and employment relations, labour legislation, trade union strategies, and public discourses in each sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".