Smart Cities Should Look ‘Smart’: Innovating Policy Towards More Liveable Telecommunications Infrastructure
Bibliographic record
Abstract
This Major Paper presents research on the physical execution of the Smart City, ICT infrastructure implementation and the role of urban design policy, using the City of Toronto as a case study. The research is focused primarily on telecommunications infrastructure in the City of Toronto. My research concerns the question of whether ICT infrastructure will negatively affect the urban design of cities. A qualitative methodology approach is applied in this research, including a literature review, policy review, site observations and semi-structured interviews with professionals in the fields of urban design, urban planning, infrastructure planning and city planning. This Paper presents a scholarly evolution of the Smart City paradigm, defining the physical components of the Smart City in the urban context. This is followed by a policy review of the specific urban design policies which guide ICT infrastructure in the City of Toronto. The bulk of this paper consists of a case study and research findings from site observations and semistructured interviews. Three themes from the policy review are presented, which guide the interpretation and analysis of field observations. A major finding is that, although there is consensus on the importance of urban design standards in policymaking for Smart City infrastructure, the City of Toronto has not sufficiently considered the urban design implications of ICT infrastructure.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".