Canadian smart cities: a case for the circular economy in the age of "smart" innovation
Bibliographic record
Abstract
The Canadian Smart Cities Challenge enabled municipalities across the country to reflect on how smart city technology can be used to solve their unique community challenges, embrace the possibility of impactful projects, create collaborations, and create a suite of digital tools. This paper analyses whether governments can be catalysts in adopting circular economy thinking in the age of digital innovation. In reviewing the SCC applications, five proposal submissions were analysed in depth against a circular economy framework. Recommendations for further development in smart city thinking centre around future Smart Cities Challenges, and building circular assumptions into the challenge questions, whereby ensuring circular principles are a priority for municipalities as they continue to grow and adapt to smart city technological advances. Key words: Smart Cities Challenge, circular economy, smart city technology, innovation, sustainable, reuse, sharing, remanufacturing and repurposing
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".