Whose smart city? : A framework and discussion guide for planning open and inclusive urban digital experiments
Bibliographic record
Abstract
‘Smart cities’ represent the integration of ‘big data’ collected via networked cameras, sensors, and meters into the urban fabric with the overarching goal of making infrastructure more efficient and improving citizens’ lives. While data has been used to support planning efforts for decades, this new paradigm of ‘urban informatics’ means that planning will increasingly be driven by data. However, the planning profession is still grappling with how existing practices might need to adapt to tackle the challenges of planning in the smart city. Accordingly, there is a gap in terms of educational resources on smart cities aimed at planning professionals. Through an action research approach involving a review of recent academic and popular literature on smart cities, this project synthesizes a set of ‘best practices’ and proposes a discussion guide for planning professionals to learn about the implications for their practice in a world where big data shapes our cities. Keywords: smart cities, urban informatics, planning ethics, Big Data, citizen participation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".