Fantasy visions, Informal Urbanization, and Local conflict: Contradictions of Smart City imaginaries in India
Bibliographic record
Abstract
Abstract Smart city imaginaries have emerged in southern cities driven by neoliberal logics in the urban space. In the Indian context, much scholarly work in India has continued to engage with sweeping accounts of cities as opposed to detailed empirical studies. In this paper, I attempt to address this gap through an in-depth ethnographic inquiry of a slum redevelopment project(part of Smart city initiative) in the city of Bhubaneswar, India. I seek to understand ways in which smart city initiatives influence the everyday life of the marginalized urban slum dwellers. Drawing on participant observation; document analysis; and semi-structured interviews, I put forth three key findings from the study that advance the notion that Smart Cities initiatives in Indian cities actively marginalize the slum dwellers by attempting to dominate through inclusion. I borrow from Foucauldian concept of ‘counter-conducts’ and its reimagination in planning by Huxley (2017) to demonstrate that smart cities discourses are counter-intuitively resulting in emergent spaces of resistance in the form of counter-hegemonic practices, thus allowing spaces for evolution of discourse from unfamiliar territories. I conclude by discussing that city planning and governance pathways in India risk creating complicated path dependencies that can lead to state-citizen conflicts in the future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.051 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".