Dirty work in the clean city: An embodied Urban Political Ecology of women informal recyclers’ work in the ‘clean city’
Bibliographic record
Abstract
and 2016 Solid Waste Management Rules, and the municipal privatization and mechanization of solid waste management practice. The study is informed by 10 months of ethnographic research and a series of interviews and group discussions with women recyclers between 2016 and 2018. Using a feminist embodied Urban Political Ecology approach, I suggest the imagining and production of the 'clean and green' world-class city is affecting Dalit women recyclers' work in two ways. First, I argue that emerging cleanliness governance mechanisms and solid waste management practices are re-spatializing and masculinizing waste labour in the city. I show how spatial, discursive and temporal shifts in solid waste management are producing new challenges for Dalit women recyclers in accessing waste, intensifying their physical and financial burdens and requiring more precarious adaptations to generate daily incomes. Second, I explore women recyclers' own clean city aspirations, expressing a desire to experience the 'clean and green' city and a simultaneous sense of betrayal as their livelihoods, communities and bodies are excluded from its imagining and material production. I suggest that an embodied intersectional analysis of waste labour reveals how the imagining and production of clean and sustainable 'modern' cities can cause damage to socially marginalized and gendered bodies as they are displaced from work and denied the substantive experience of urban citizenship in the 'world-class' city. Attention to embodiment thus deepens an understanding of the complexities and contradictions invoked in urban environmental governance and infrastructural transformations, informing the imagining and production of more equitable and reparative urban futures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".