(Re)Prioritizing Citizens in Smart Cities Governance: Examples of Smart Citizenship from Urban India
Bibliographic record
Abstract
By examining the community-focused informatics work of Transparent Chennai (TC) (India) we seek to contrast the Smart Cities agenda — with its focus on the consumption and commercialization of digital technologies and infrastructure — to citizen-driven approaches, what we term, Smart Citizenship. A Smart Citizenship approach engages citizens in complementary digitally mediated and face-to-face processes that respect local knowledge systems. We devise a framework for understanding Smart Citizenship and link this to our case study of Transparent Chennai. Our research identifies how information and communication technologies (ICTs) can serve to spotlight overlooked or undervalued urban infrastructural, planning and environmental issues — such as the need for access to safe and clean public toilets; road safety and pro-pedestrian planning. We conclude by suggesting that a locally grounded Smart Citizenship agenda can reprioritize the needs and interests of local communities and neighbourhoods in urban governance, rather than those of exclusivist private commercial interests.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".