Toronto’s Not-So-“Smart” City: Dismantling the Tech Utopia & Building Stronger Communities
Bibliographic record
Abstract
This thesis critically analyzes the dominant discourse, actors, and technologies associated with the Sidewalk Toronto smart city project to uncover and resist the potential dangers of the unregulated smart city. Drawing from gray and scholarly literature alongside four semistructured interviews and three action research methods, this research shows that smart cities and technologies are the latest iteration of corporate power, exploitation, and control. Imbued with neoliberal, colonial, and positivistic logics, the smart city risks further eroding democracy, privacy, and equity in favour of promoting privatization, surveillance, and an increased concentration of power and wealth among corporate and state elite. While the publicized promise of the smart city may continuously shift to reflect and co-opt oppositional narratives, its logics remain static, and its beneficiaries remain few. Applying a social justice-oriented lens which connects critical theory, postmodernism, poststructuralism, intersectional feminism, and anticolonial methodologies is crucial in reconceptualizing “smartness” and prioritizing public good.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".