The Push to Promote the Smart City: Assessing the Impacts of a Government-Led Smart City Challenge
Bibliographic record
Abstract
Cities worldwide continue to experiment with urban technology solutions to address urban problems. Smart city projects have recently emerged in North America, including Canada, but their governance implications on cities and communities remains not fully understood. This article is based on a post-hoc analysis of a pan-Canadian government-driven technology and innovation competition called the Smart Cities Challenge (SCC). The research explored the impact of the SCC across three areas: (1) municipal innovation and governance, (2) collaboration among public, private, and community networks, and (3) policy implementation within local government structures. The findings indicate that public smart city competitions do succeed in fostering strategic planning and community engagement in municipal governments, however local governments struggle to advance technology-oriented projects to the implementation stage without greater investment. For those that did, sustaining successes without further investment and capacity building. The study’s key contribution lies in revealing how the competition stimulated new governance processes, strategic planning efforts, and collaborative networks, while also exposing significant implementation challenges rooted in fiscal constraints, limited technical capacity, and the short-lived nature of competition-based incentives. The research highlights the promise and limits of government-led smart city competitions as tools for advancing municipal innovation in today’s constrained fiscal context.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".