Public Lives, Private Water: Female Ready-Made Garment Factory Workers in Peri-Urban Bangladesh
Bibliographic record
Abstract
Abstract In Dhaka city and its fringe peri-urban sprawls water for domestic use is an increasingly contested commodity. The location of our research, Gazipur district, bordering the growing city of Dhaka, is the heartland of Bangladesh's Ready Made Garments (RMG) industry, which has spread unplanned in former wetlands and agrarian belts. However, unlike Dhaka, the almost fully industrialized peri-urban areas bordering the city, like many other such areas globally, function in an institutional vacuum. There are no formal institutional arrangements for water supply or sanitation. In the absence of regulations for mining groundwater for industrial use and weakly enforced norms for effluent discharge, the expansion of the RMG industry and other industries has had a disproportionate environmental impact. In this complex and challenging context, we apply a political economy lens to draw attention to the paradoxical situation of the increasingly "public" lives of poor Bangladeshi women working in large numbers in the RMG industry in situations of increasingly "private" and appropriated water sources in this institutionally liminal peri-urban space. Our findings show that poorly paid work for women in Bangladesh's RMG industry does not translate to women's empowerment because, among others, a persisting masculinity and the lack of reliable, appropriate and affordable WASH services make women's domestic water work responsibilities obligatory and onerous.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".