Why Smart Cities are so 2017 (and what this means for urban transport innovation)
Bibliographic record
Abstract
There has been significant policy interest in Smart Cities as a means of harnessing the power of new IT solutions, urban sensors and Big Data to provide services more efficiently. But Smart Cities are part of a broader set of initiatives with a long history in urban technology and planning to try and generate innovation. Whilst data-driven service delivery initiatives are succeeding on their own, so-called living laboratories, knowledge precincts and other techno-utopian dreams that try to create a holistic Smart City have usually fallen short of expectations. Today's most interesting experiment is Google's Sidewalk Labs Quayside development in Toronto, where the firm is trialing tech solutions to urban problems, including shared mobility. This paper explores what underpins the Toronto experiment, describes what is happening with other Smart City initiatives, and provides critiques of Smart City philosophy from key urban theorists. This is used to explore what it means for innovation in urban mobility, and to identify a set of issues that require resolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".