Canadian smart cities: Are we wiring new citizen‐local government interactions?
Bibliographic record
Abstract
Governments around the world are developing smart city projects, with the aim to realize diverse goals of increased efficiency, sustainability, citizen engagement, and improved delivery of services. The processes through which these projects are conceptualized vary dramatically, with potential implications for how citizens are involved or engaged. This research examines the 20 finalists in the Canadian Smart Cities Challenge, a Canadian federal government contest held from 2017 to 2019 to disburse funding in support of smart city projects. We analyzed each of the finalist proposals, coding all instances of citizen engagement used to develop the proposal. A significant majority of the proposals used traditional types of citizen engagement, notably citizen meetings, round tables, and workshops, to develop their smart city plans. We also noted the use of transactional forms of citizen engagement, such as apps, and the use of social media. Despite the general rhetoric of innovation in the development of smart cities, this research finds that citizens are most commonly engaged in traditional ways. This research provides cues for governments that are developing smart city projects, placing an emphasis on the importance of the process of smart city development, and not simply the product.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.041 | 0.021 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".