Examining slow and spectacular forms of violence through the politics of redevelopment in Kamathipura
Bibliographic record
Abstract
This paper analyses the contested nature of the redevelopment of historic red-light neighbourhoods and their impact on social–moral–economic relations, using the case study of Kamathipura in Mumbai, India. Specifically, this article highlights the contested nature of the attempted redevelopment of a historic, inner-city ‘red light’ neighbourhood showcasing two kinds of interconnected violence—slow (such as deterioration of infrastructure and dilapidated neighbourhoods due to state neglect) and spectacular (such as massive and planned urban restructurings and spatial transformations)—both founded on a moral argument for sanitising and commodifying space. While redevelopment plans remain largely on paper, the speculation seizes the neighbourhood and restructures social–moral–economic relations causing great harm to vulnerable groups, while leaving several others in a debilitating limbo. We argue that the moral stigma attached to historically marginalised red-light neighbourhoods creates a paradoxical situation where it both prevents sustained municipal intervention and catalyses large-scale redevelopment proposals that mask the insidious violence of neglect by the state. We develop this argument through an in-depth field study drawing from interviews, focus group discussions and life histories conducted between 2014 and 2019 with a range of groups working and living in Kamathipura, one of Asia’s largest and oldest red-light areas located in the island city of Mumbai. This paper traces the complex interlinkages between different forms of violence(s) and the moral regimes that enable and facilitate them through contested claims to the neighbourhood and its uncertain future.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".