The remaking of working classes: digital labour platforms and workers’ struggles in the Global South
Bibliographic record
Abstract
This themed issue explores the impact of digital labour platforms on the conditions of work and social reproduction in the Global South. The collection of articles – most of which derive from research undertaken by the Future of Work(ers) research group, led by the Southern Centre for Inequality Studies at the University of the Witwatersrand – profile case studies from Argentina, Brazil, India, Kenya, South Africa and Uganda. The case studies focus on location-based platforms in food delivery, e-hailing, e-commerce and beauty and spa work. The collection of articles explores two broad and overlapping themes. The first is how platform business models are redefining the work process and the conditions of work. Here, labour process theory is particularly relevant because it allows us to identify both the points of value production and the sources of working-class power (Kenny and Webster, 2021). The second is the transformative possibilities and limitations of emerging forms of worker organisation. Here the power resources approach is especially useful because it allows us to analyse the extent to which organisations can leverage sources of power to transform the conditions of work and social reproduction. Ultimately, the contributions illustrate that the impact of digital technologies on the world of workers is neither predetermined nor linear. Rather it is shaped by struggle, the terms of which are themselves contingent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".