Review of Supriya RoyChowdhury (2021) City of Shadows: Slums and Informal Work in Bangalore
Bibliographic record
Abstract
Packed with new findings about India's urban development, this book advances our understanding of informal work and informal workers.Although not a long book, at just over 200 pages, it offers very detailed empirical evidence and, importantly, contains a new insight about the nature of spatial and class-based dynamics in different urban slums, which I explain below.As the sub-title suggests, the book is about slums and informal work in Bangalore (Bengaluru), India's fourth most populous city and the local version of Silicon Valley (p.14).Beneath such glamour are the slums that constitute the city's underbelly.Although smaller proportionally than other large Indian cities, Bangalore's slums have grown rapidly.The book is structured into nine chapters, including the Introduction, which asks what should be "an appropriate conceptual framework for imagining urban marginalities?" (p.1), among other questions.The exclusion of slum-dwellers is particularly confronting in Bangalore, given the city's apparent success, raising the critical question: "[What] happens to the poor in a city that is rapidly growing rich?" (p. 6).Chapters 2 and 3 provide conceptual overviews of workers, the welfare state, and informality.Chapter 4 offers a nice summary of Bangalore's political economy.Chapters 5 to 8 offer detailed empirical accounts, which represent the core of the book's contribution.Chapter 5 looks at "new slums" on the city's urban outskirts where people have lived, on average, for the past decade.It offers a detailed descriptive account of four slums, based on household surveys and focus groups.The chapter details low wages, low levels of literacy, and marginalisation from labour markets that present even a slim chance of improving living standards.Electricity, running water and basic sanitation are typically absent.Poor access to school education further entrenches intergenerational disadvantage.Residents were typically reluctant to admit poor school attendance, representing a social stigma among parents that is handled with great skill and empathy by the author.In terms of formal politics, party apparatchiks typically ignore slumdwellers outside elections.Chapter 6 looks at "old slums" in the inner city, which have existed for decades, some for up to 70 years.Their longevity is reflected in a semi-formal status as 'notified slums', meaning that government agencies are responsible for the provision of at least some basic amenities.Again, this chapter is impressively detailed, drawing data from a study of 300 households across six slums in the city's central commercial hub.An interesting finding is the significant economic inequality within slum communities, which house workers ranging from skilled tradespeople (electricians, plumbers, welders, and so on) to much lower-paid, lower-status workers employed as headload bearers,
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".