A theatre of machines: Automata circuses and digital bread in the smart city of Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it