Towards the right model of smart city governance in India
Bibliographic record
Abstract
There is broad agreement among the scientific community that local government's play a vital role in fostering smart cities which focusses on improving quality of life by integrating technology with the built environment. But, urban governance in rapidly urbanising countries of global south is often poorly organised to deal with complex urban challenges, severely hindering their aspirations to become smart cities. Although smart city dossiers are abundant in literature, their governance framework and structural variations in such development across regions is lacking. Furthermore, efforts to import governance models from developed world cities are facing lack of unique context sensitivities, which stand against their transformation as smart cities. This paper contributes to the debate on urban governance of smart cities by providing their distinct theoretical conceptualisations and linking them with case studies. It analyses the urban governance dynamics in Indian cities which has started implementing a massive 100 smart cities development programme. From the past experiences of Indian cities in reforming urban administration to its new model of special purpose vehicle led project execution; this research critically assesses the ability of Indian cities to transform their traditional bureaucratic governments into a more accountable collaborative governance. The outcomes from this study highlight the need for aspiring smart cities in emerging economies to address deep-seated structural issues of municipal government's and engage in the process of governance transformation rather than adopting temporary solutions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".