Rethinking public participation in the smart city
Bibliographic record
Abstract
In efforts to become “smart cities,” local governments are adopting various technologies that promise opportunities for increasing participation by expanding access to public comment and deliberation. Scholars and practitioners encounter the problem, however, of defining publics—demarcating who might participate through technology‐enhanced public engagement. We explore two case studies in the city of Calgary that employ technologies to enhance public engagement. We analyzed the cases considering both the definition of publics and the level of citizen participation in areas of participatory budgeting and secondary suites. Our findings suggest that engaging the public is not a straightforward process, and that technology‐enhanced public engagement can often reduce participation towards tokenism. City councillors and planners need to critically confront claims that smart cities necessarily enhance participation. Moving beyond tokenism requires understanding “public” as a plural category. Municipal governments should seek to proactively engage citizens and communities utilizing helpful resources including, but not limited to, digital tools and smart technologies. This would allow planners to keep a “finger on the pulse” of publics' concerns, better identifying and addressing issues of equity and social justice. It is also important to consider how marginalized publics can best be recognized in order to bring their concerns to the fore in decision‐making processes.
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.077 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.006 | 0.008 |
| 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".