The Knowledge Instruments in Smart City Legitimation and Critique: A Pragmatic-Sociology Assessment of the Case of Sidewalk Toronto
Bibliographic record
Abstract
In recent decades, the smart city concept has gained momentum in policy and popular discourses. Largely a long-term marketing effort grounded in private technology firms such as IBM and Cisco (Söderström 2014, Hollands 2015), the meanings of and the actual/potential material assemblages involved in the idea have received sustained academic criticism. At once under the purview of urban studies, software studies, STS, law, and marketing, the smart city idea as it was written seems to be as plastic as it is multi-faceted. This thesis explores the ways in which the idea of the smart city has been conceived through a literature review, and a case study of Sidewalk Toronto, a project conceived in October 2017 and terminated in May 2020. The review explores the conception of the smart city as, for example, algorithmic governmentality (Leszczynski 2016), as a threat narrative (White 2016), and as cybernetic urbanism (Krivy 2018). It identifies three leitmotifs of the idea as a conceptual movement: the presumption of the existence of ‘raw data,’ a dataism-driven urban teleology, and a theory-practice of post-politics governance. This study adapts Luc Boltanski’s pragmatic sociology as framework and provides an identification of the smart city knowledge constructs, including ‘city-as-platform,’ ‘urban data,’ and ‘civil data trust’ that defined the controversy around Sidewalk Labs. These constructs were used to justify the project in Sidewalk Lab’s marketing and appeasement efforts; and to produce modes of criticisms counterposed by civil society actors, between 2017 and 2020.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.038 | 0.046 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".