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Record W4379538151 · doi:10.15173/glj.v14i2.5098

Precarious Work and the Gendered Individualisation of Risk in the South African Manufacturing Sector, 2002–2017

2023· article· en· W4379538151 on OpenAlexvenueno aff
Siviwe Mhlana

Bibliographic record

VenueGlobal Labour Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringInformal sectorPrecarious workUnemploymentEconomic restructuringPoliticsPrecarityLabour economicsEarningsEconomicsJob securitySociologyWork (physics)Political economyPolitical scienceGender studiesEconomyEconomic growth

Abstract

fetched live from OpenAlex

Against the backdrop of workplace restructuring globally, post-apartheid South Africa is experiencing consistently high levels of unemployment, the deterioration of employment security and limited improvements in earnings. This article investigates the changing nature of labour-intensive production in the South African labour market and the gendered individualisation of risk associated with precarious or non-standard forms of employment. The article expands on the critical theoretical narrative about the challenges of labour under neo-liberalism by applying a gendered political economy analysis to the experiences of precariousness among workers in the South African manufacturing sector. By focusing on the interconnections between gender and political economy, this article delinks questions about the crisis of labour from a narrow focus on skills and refocuses our understanding in terms of the structural determinants of vulnerabilities in the labour market. The article argues that the gender composition of informal and precarious work in the post-apartheid labour market has significant implications for addressing the persistent racialised and gendered inequalities in the South African economy. KEYWORDS: labour market restructuring; informal employment; precarious work; gender JEL CLASSIFICATION: J21; J30; J71; J80

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.060
GPT teacher head0.349
Teacher spread0.289 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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