Working-class Commuters and Innovative Use of Associational Power: The Case of Mamelodi Train Sector in South Africa
Bibliographic record
Abstract
The article analyses the power resources of working-class train commuters in Tshwane, South Africa. I examine the organisational strategy of Mamelodi Train Sector (MTS), considering the crisis of representation characteristic of the South African labour movement currently. With changing composition of membership in the Congress of South African Trade Unions, the article begins with the question, what strategies and avenues are there for both unions and unorganised members of the working class? Through the case study of MTS, I suggest a need to rethink power resources and strategies in order to appreciate various non-union ways in which workers continue to organise under conditions that are at times hostile to unions. Drawing on the Power Resources Approach, I argue that MTS can utilise its strategic site of operations and associational power to link unorganised workers with relevant unions. Through its on-train organising, MTS strategically uses its associational power to achieve two things: 1) to empower non-unionised workers, and 2) to influence local-level politics during the morning and afternoon commutes. KEYWORDS: Mamelodi Train Sector; train; comrades’ coach; associational power; political agency
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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 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".