The Production of Smart Cities: An Analysis of Barcelona and Toronto
Bibliographic record
Abstract
The paper examines the discursive, governance, and territorial strategies of smart city initiatives, focusing on the comparative analysis of Barcelona and Toronto. By analyzing the narratives, systems of governance, and geographical consequences of these technological changes, the research uncovers the intricate and difficult aspects of the idealistic concept of smart cities. Barcelona's citizen-centric strategy, which prioritizes participation and municipal control, stands in contrast to Toronto's corporate-driven approach, underscoring notable disparities in social equity and stakeholder engagement. The results emphasize the significance of inclusive and participatory governance structures in guaranteeing that smart city projects contribute to equitable and sustainable urban development. Furthermore, the study explores the profound implications for urban planners, who are required to include innovative technology, foster cross-disciplinary collaboration, and tackle challenges related to digital exclusion, privacy, and community cohesion. This research proposes a balanced approach to smart city development that combines technology developments with social justice and environmental sustainability. By drawing lessons from Barcelona and Toronto, the aim is to create urban futures that are more democratic and resilient urban futures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".