Smart Cities as Hybrid Spaces of Governance: Beyond the Hard/Soft Dichotomy in Cyber-Urbanization
Bibliographic record
Abstract
This paper problematizes the dichotomy of hard (technocratic) and soft (societal) approaches to the smart city. Smart cities are reviewed as hybrid spaces that transcend the sum of the social and the technical. By providing platforms for enabling, monitoring, digitalizing, formalizing, and amassing information about collective and personal experiences and behaviors, smart cities accelerate the customization of existing urban services and establish new spaces of socialization, accumulation and regulation, including in hitherto hard-to-reach realms of everyday and personal life. These experiences signify the emergence of cyber-physical-social spaces, featuring the hybridization of the digital, governance, and sociocultural domains. The production of such hybrid spaces of governance is reviewed through 50 urban-level strategies for smart cities in different countries across the world. The analysis confirms the tendencies towards a hard/soft fusion and the ever-deepening interpenetration of the digital, physical, and social elements in smart cities. This suggests epistemological problems of separating the hard and soft domains. However, this integration still creates political and analytical tensions that are arguably evident in the early stages of the digital transition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".