‘Built from the internet up’: assessing citizen participation in smart city planning through the case study of Quayside, Toronto
Bibliographic record
Abstract
Abstract Citizen participation in smart cities has come under ever more scrutiny in recent years. Whilst smart city projects across the world have proclaimed themselves as citizen-centric, scholars have found that these claims are still framed within a neoliberal, post-political conception of citizenship, whereby citizens are afforded little agency. In evaluating such projects and in aid of developing a better understanding of the citizen’s role in smart cities, scholars have developed various heuristics. This paper aims to further both empirical and theoretical developments in the field to evaluate citizen participation in Quayside, Toronto’s first smart city neighbourhood, using Cardullo and Kitchin’s Scaffold of Smart Citizen Participation. A document analysis of seventeen citizen engagement summary reports and advertisements, corresponding to eight citizen engagement initiatives, has revealed that the quality of citizen participation varied substantially according to individual initiatives in Quayside. It was also discovered that Cardullo and Kitchin’s scaffold was ineffective at capturing the complexity of citizen engagement in smart city planning. In light of this, a new heuristic which assesses the post-political spaces of citizen engagement has been developed. This heuristic can provide a productive foundation for further research in the field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".