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Record W4210665259 · doi:10.15173/glj.v13i1.4346

Working-class Commuters and Innovative Use of Associational Power: The Case of Mamelodi Train Sector in South Africa

2022· article· en· W4210665259 on OpenAlexvenueno aff
Mpho Mmadi

Bibliographic record

VenueGlobal Labour Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Agency (philosophy)PoliticsWorking classRepresentation (politics)Order (exchange)Class (philosophy)SociologyPolitical economyPolitical scienceBusinessLawSocial science

Abstract

fetched live from OpenAlex

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

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.493

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.277
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2022
Admission routes1
Has abstractyes

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