The Collective Working Body: Rethinking Apparel Workers' Health and Well-being during the COVID-19 Pandemic in Sri Lanka
Bibliographic record
Abstract
This article contributes to debates on global apparel workers’ health and well-being through an examination of how Sri Lankan workers were affected and treated during the COVID-19 pandemic. Based on qualitative interviews in and around the Katunayake Export Processing Zone, the article takes the Sri Lankan apparel industry as a case study. It reconceptualises the “precarious working body” as a “collective body” in order to demonstrate how workers’ health was a matter of collective precariousness. Workers’ health was not only dependent on that of others around them inside densely populated factories, but was also shaped by systemic material and discursive practices that affected workers collectively. These material practices included labour control and incentive structures that prevented workers from seeking medical attention and taking leave when needed, which in turn led to the spread of the virus across factories. The discursive practices comprise the social stigma and devaluation of women apparel workers that facilitated the blaming of workers for spreading the virus and enabled their inhumane treatment during the pandemic response. We argue that conceiving of apparel workers as a “collective body” enables a recognition of the systemic forces that create ill health at work and that expose certain (but not all) working bodies to the risks of infection. KEYWORDS: labour regimes; social stigma; occupational health; apparel industry; COVID-19
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".