Public engagement in smart city development: Lessons from communities in Canada's Smart City Challenge
Bibliographic record
Abstract
Quality of life is often touted as the main benefit of building smart cities. This, however, raises questions about the extent to which the public is engaged as part of the “smart” development process, particularly given the significant financial investments often required to meaningfully design smart city projects. To better understand approaches to public engagement in the context of smart city development, we draw upon three selected finalists of Infrastructure Canada's Smart City Challenge, which invited municipalities, regional governments, and Indigenous communities to enter a competition where the winning proposals would be awarded federal financial grants to complete their projects. Prizes of $5 million, $10 million, and $50 million were awarded. Specifically, we compare the public engagement experiences of the Mohawk Council of Akwesasne (Quebec), the City of Guelph, and the Region of Waterloo. We carried out semi‐structured interviews and reviewed documents in each community to better understand how finalists in each category engaged residents in proposal development. The paper addresses how communities are approaching public engagement in smart city development and the implications of these approaches. We conclude that, despite earnest attempts to publicly engage and become citizen‐centric, municipal governments continue to see civic participation as a top‐down tool .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.084 | 0.024 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".