Trade Union Transformation and Informal Sector Organising in Uganda: The Prospects and Challenges for Promoting Labour-led Development
Bibliographic record
Abstract
Despite celebrations from governments, corporations and international financial institutions around increasing economic growth, the majority of the world’s urban labour force continues to work under informal conditions, lacking enforceable contracts, adequate earnings, democratic representation, secure employment and social protection. The pervasiveness of informal labour globally has given rise to numerous calls to adopt a wider and more diverse understanding of what constitutes labouring classes and what is required to organise them. Our case study assesses the outcomes and effectiveness of informal sector organising in Uganda, focusing on the transportation, market and textile sectors. Drawing on Guy Standing’s distinction between “business” and “community” unions and Benjamin Selwyn’s contrasting of “capital-centred development theory” (CCDT) and “labour-led development” (LLD), we argue that community unionist approaches are most effective in addressing the decent work deficit in the informal economy. Simultaneously, the trade unions face constant barriers to successful community organising in the informal economy that cannot be easily overcome without wider changes to the structural conditions under which union organisers must operate. KEYWORDS: Trade unionism; informal labour organising; labour-centred development; Uganda; decent work
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".