How Canadian Municipalities are(n't) engaging the public in smart city initiatives
Bibliographic record
Abstract
The smart city concept is innovation in urbanism. Innovation is transformative, demanding the involvement of the public based on a belief that those who will be impacted by a decision have a right to be involved in the decision-making process. But smart city initiatives raise complex technical, privacy, economic, and intellectual property issues unlike those the public has been presented with before. This paper explores how Canadian municipalities are approaching this challenge by coding and analyzing applications to Infrastructure Canada’s Smart Cities Challenge (SCC). The analysis reveals, among other findings, that municipalities engaged citizens directly as well as their representatives, leveraged previously-conducted engagement and conducted new engagement, and employed a range of engagement activities online and offline. Recommendations to Infrastructure Canada and municipal planners highlight the need for more public input on the technology solutions proposed, increased attention to the digital divide during engagement, and citizen involvement in all stages of open innovation. Key words: Smart Cities Challenge, public engagement, open innovation
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.045 | 0.014 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".