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Record W3168903287 · doi:10.15173/glj.v12i2.4394

Trade Union Transformation and Informal Sector Organising in Uganda: The Prospects and Challenges for Promoting Labour-led Development

2021· article· en· W3168903287 on OpenAlexfundvenueno aff
Tobias Gerhard Schminke, Gavin Fridell

Bibliographic record

VenueGlobal Labour Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersCanada Research Chairs
KeywordsInformal sectorTrade unionWork (physics)EarningsDemocracyEconomicsFace (sociological concept)Economic growthLabour economicsSociologyPolitical scienceFinance

Abstract

fetched live from OpenAlex

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

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 score0.827
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.263
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

Citations5
Published2021
Admission routes2
Has abstractyes

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