Knowing and governing smart cities: Four cases of citizen engagement with digital urbanism
Bibliographic record
Abstract
Research on smart urbanism predominantly focusses on the production of digital knowledge. In response, this paper probes the potential and limitations of digital devices producing the kinds of knowledge needed for governing urban environments. Based on four case studies in Europe, the paper investigates what kinds of knowledge become privileged and what kinds of knowledge get overlooked when digital devices are deployed to inform urban governance. We find that non-digital knowledges are easily eclipsed, yet remain vital to effective and inclusive urban environmental governance. Our findings suggest that digital technologies need to be developed in ways that are attentive towards the different kinds of knowledge (digital and non-digital) that may be necessary for effective and inclusive urban governance. This holds for the knowledges that are used to develop digital devices and the knowledges intended to be generated through them, as well as openness towards unanticipated or overlooked knowledges that still matter.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.038 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".