A theatre of machines: Automata circuses and digital bread in the smart city of Toronto
Bibliographic record
Abstract
In this paper, the policies, projects, and promises of “smart” initiatives at the City of Toronto are evaluated, as they manifest through a technological convergence between local government services and an increased focus on citizen services through data‐driven mediums. Through direct participant observation and formal interviews, a robust understanding of the internal institutional dynamics, the perspectives citizens in the “smart city,” and the operational disconnects in governance, policy, and practice has been gained. Our case study on the City of Toronto provides an account of how and from where these smart motivations for increasing a data‐driven engagement with the public have arisen over the past several years. In doing so, we identify key characteristics that both enable and hinder the existing smart city in the forms of access to open data, the use of increased computational methods, and the engagement of public services through digital space as requirements for the future of participatory governance. We argue that instituting appropriate policies and engaging citizens to co‐design and participate in the planning processes are essential to ensuring an inclusive, modern, and open smart city .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".