Transforming Transport Unions through Mass Organisation of Informal Workers: A Case Study of the ATGWU in Uganda
Bibliographic record
Abstract
This paper analyses the power resources of informal transport workers in Uganda, and the transformation processes of the Amalgamated Transport and General Workers' Union (ATGWU) and their newly affiliated informal workers' associations in organising informal workers. We examined the process of organisation, how strategic choices were made, and how the expected increases in power resources were realised. We also analysed the critical factors behind the success of the strategy, as well as the lessons learned and the unresolved challenges. The ATGWU faced an almost complete collapse in membership following the impact of structural adjustment programmes in the 1980s, and the subsequent informalisation of the transport industry. In recent years, it has pioneered a strategy of organising through the affiliatio of mass-membership associations of informal workers, notably representing minibus taxi workers and motorcycle taxi ("boda-boda") riders. The unionisation of informal workers has had a dramatic impact: a reduction in police harassment, substantial gains through collective bargaining, reduced internal conflict within the associations, and improvement of visibility and status for nformal women transport workers. The rapid expansion has raided new challenges for the union, particularly in the transition to a fully integrated formal-informal organisation, the need for reform of democratic process and accountability, and the maintenance of solidarity between informal and formal workers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".