Urban neoliberalism, smart city, and Big Tech: The aborted Sidewalk Labs Toronto experiment
Bibliographic record
Abstract
On May 7, 2020, Sidewalk Labs (part of Alphabet, which includes Google) abandoned its Toronto waterfront redevelopment project after two-and-a-half years of planning, public relations, and bargaining with the public agency responsible for this sector. The official and final reason Sidewalk gave for its withdrawal was the uncertainty of the Toronto real estate market due to the COVID-19 pandemic. Few observers of the Toronto scene subscribed to this explanation. Events did not unfold as Sidewalk would have hoped for. Its Toronto venture exposes implementation difficulties of a form of neoliberalism combining the smart city model with an active involvement of Big Tech. The Toronto narrative suggests that while the materialization of this version of neoliberalism is advantaged by plentiful resources, futurist visions of the city, and access to new technology, it is not immune to implementation hurdles associated with the context-specific nature of neoliberal projects. The paper identifies three categories of obstacles that have hampered the reaching of the Sidewalk objectives in Toronto: opposition movements objecting to electronic surveillance, corporate control, and restrictions to democratic processes; the fragmentation of the neoliberal political block; and ill-advised strategies on the part of Sidewalk.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".