Building Green: Environmental Architects and the Struggle for Sustainability in Mumbai
Bibliographic record
Abstract
Building Green explores the experience of environmental architects in Mumbai, one of the world’s most populous and population-dense urban areas and a city iconic for its massive informal settlements, extreme wealth asymmetries, and ecological stresses. Under these conditions, what does it mean to learn, and try to practice, so-called green design? By tracing the training and professional experiences of environmental architects in India’s first graduate degree program in Environmental Architecture, Rademacher shows how environmental architects forged sustainability concepts and practices and sought to make them meaningful through engaged architectural practice. The book’s focus on practitioners offers insights into the many roles that converge to produce this emergent, critically important form of urban expertise. At once activists, scientists, and designers, the environmental architects profiled in Building Green act as key agents of urban change whose efforts in practice are shaped by a complex urban development economy, layered political power relations, and a calculus of when, and how, their expert skills might be operationalized in service of a global urban future. “Highly germane to our times, Building Green examines the role of urban ecology in envisioning new kinds of sustainable cities.” CHRISTINA SCHWENKEL, University of California, Riverside “A lucid and rich ethnography of environmental architects in Mumbai.” NIKHIL ANAND, author of Hydraulic City: Water and the Infrastructures of Citizenship in Mumbai ANNE RADEMACHER is Associate Professor of Environmental Studies and Anthropology at New York University. Her books include Reigning the River: Urban Ecologies and Political Transformation in Kathmandu, Ecologies of Urbanism in India: Metropolitan Civility and Sustainability, and the edited volume Places of Nature in Ecologies of Urbanism.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".