Slum Evictions through the Lens of Labour: Capture, Value Generation, and Belonging in the Indian City
Bibliographic record
Abstract
Abstract Following the lead of labour movements, this article frames slums as labour geographies whose evictions constitute the devaluation of labour in spatial terms. This devaluation occurs in two modes: in the first, through the rendering of workers as “encroachers” or “the urban poor” in policy documents and public discourse, thereby unmooring workers from their geographies of reproduction. In the second, evictions entail the material erasure of the unwaged labour of autoconstruction and other activities that produce and sustain these geographies. Through ethnographic research in Chennai, India, and archival work, “capture” emerges as an embodied practice and emic concept that makes visible value generated through the reproduction of labour power, and the labour sunk into urban lands where access to the realm of the formal is foreclosed. Visibilising this labour of caste‐marginalised working classes is a key way by which discourse on who belongs in the city can be transformed.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".