Building a Smart City: Insights and Perspectives from the Winners of Smart City Challenge
Bibliographic record
Abstract
When Canadian Government launched smart city initiative over 130 municipalities, large and small have submitted their innovative ideas. On May 14, 2019, the four winners of the Canadian Smart Cities Challenge were announced in Ottawa: Town of Bridgewater, Nova Scotia winning $5M Prize Category; the Nunavut Communities, Nunavut winning $10M Prize Category; the City of Guelph and Wellington County, Ontario winning $10M Prize Category and the City of Montréal, Quebec winning $50M Prize Category. This paper focuses on these four cities by exploring two main research questions: (1) what does “smart cities” mean for Canadian municipalities; and, (2) what are the elements of smart city initiatives in Canada? This paper aims to build an understanding of smart city initiatives in Canadian context and explore what smart city means for Canadian municipalities. Based on previous research, the main areas explored in this research are categorized in eight aspects including (1) technology, (2) management and organization, (3) policy context, (4) governance, (5) people and communities, (6) economy, (7) built infrastructure and (8) natural environment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".