Sidewalk and Toronto: Critical Systems Heuristics and the Smart City
Bibliographic record
Abstract
‘Smart cities’, urban development projects that design computational systems and sensory technology to monitor activity and regulate energy consumption and resource distribution, are a frontier for the prospective deployment of ICTs for sustainability. Often reduced to technological problems of optimization, these projects have implications far beyond narrow environmental and consumptive frames of sustainability. Studying them requires frameworks that support us in examining technological and environmental sustainability dimensions jointly with social justice perspectives. This paper uses Critical Systems Heuristics (CSH) to examine the design of Sidewalk Toronto, an ongoing smart city development. We explore how the professed values guiding the project are contentiously enacted, and we argue that key stakeholders and beneficiaries in the planning process significantly constrain the emancipatory and transformative potential of the project by marginalizing the role of residents in determining project purposes. This analysis contributes an example that illustrates the relevance of critical systems thinking in ICT4S and offers CSH as a conceptual frame that supports critical reflection on the tensions between the visions and realities of ‘sustainable’ ways of organizing human life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".