Bibliographic record
Abstract
As communities around the world invest in new digital technologies, many are guided by enthusiasm for what Rob Kitchin calls ‘the real-time city’. From connected traffic lights that adjust to the flow of vehicles to digital platforms that allow residents to track snowplows in their area, these ‘smart city’ technologies promise ‘real-time’ updates and ‘real-time’ control. But what does it mean for a technology or a city to operate in ‘real-time’, and what is the relationship between ‘real-time’ and the utility or intelligence of digital systems? What does excitement over ‘real-time’ overlook? To answer these questions, this paper explores the discursive function of ‘real-time’ within the context of Sidewalk Toronto, Canada's most prominent foray into smart city development. Drawing on the project's initial proposal and subsequent development plan, it suggests that the discursive function of ‘real-time’ was threefold. First, references to ‘real-time’ affirmed the immediacy or objectivity of ‘smart city’ technologies. Second, enthusiasm for ‘real-time’ affirmed the objectification and commodification of time itself. Third, references to ‘real-time’ aided in the de-contextualization and de-politicization of algorithmic decision-making, collapsing urban governance into a ‘perpetual present’ in which ‘real-time’ constituted no time (or space) at all. The paper closes by exploring the politics of this ‘real-time city’, and the hierarchical and technocratic forms of governance that characterized the Sidewalk Toronto project.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.055 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".