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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".