The Politics of Space and Everyday Surveillance in a Delhi Homelessness Shelter: An Ethnographic Exploration
Bibliographic record
Abstract
In this paper, I offer a unique lens through which to theorize neoliberal governance from the perspective of the politics of space and everyday surveillance in Akash Sadan, a Delhi homeless shelter. Drawing from interview data and over two years of ethnographic research with children (boys and girls), their families, and civil society and state representatives, I bring theories of homelessness, surveillance, and spatialized control as they connect to scholarship on governance, regulation, and stigmatization into conversation with Miraftab’s (2004) framework on the mutually constitutive concepts of “invited” and “invented” spaces of participation. Extending this framework, I conceptualize the Aakash Sadan shelter as an “invited,” state-sanctioned space supported by a state-legitimized non- governmental organization (NGO). I argue that regulatory power operates through spatial territorialism and stigmatized surveillance exercised by an NGO primarily in league with the state. This stigmatization is inseparable from the community’s historically enforced precarity and state-induced dispossession. The empirical case of the Aakash Sadan shelter thus demonstrates how the convergence of these spatial features in a state-sanctioned community can leave residents with reduced access to basic and urgently needed services, heightening experiences of poverty and precarity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".