Expanded Urban Planning as a Vehicle for Understanding and Shaping Smart, Liveable Cities
Bibliographic record
Abstract
Smart city is currently a trendy concept that has been promoted by many international companies, universities and cities, such as IBM, CISCO, MIT, Shanghai, as well as the European Union. This top down, technocratic approach has been severely criticized in many academic publications. Concurrently there is an increasing buzz emerging from citizens – women and men, who are involved in the application of community informatics for self-organization in urban settings. Consequently, the smart city as a contested concept and an initiative is under social and political construction. We argue that the smart city can be better understood and implemented, when framed from a holistic and integrative perspective as a multi-scalar and multi-dimensional endeavor that is approached through “expanded urban planning”. The aim of the article is to present and discuss the expanded urban planning approach as an alternative story to smart cities. The relevance of this approach is assessed in the light of a case study of Designing for the Smart City, a course for future architects and planners, at the Politecnico di Milano, Italy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".