Urban neoliberalism, smart city, and Big Tech: The aborted Sidewalk Labs Toronto experiment
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
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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