Smart city and resilient city: Differences and connections
Bibliographic record
Abstract
Abstract Smart city (SC) and resilient city (RC) have been studied and practiced over the years in terms of the increasing urban problems and disasters. However, there is a large overlap between their meanings and relationships. With an increasing concern for both SC and RC in urban development and hazard mitigation, a review was conducted to explore the differences and connections between SC and RC with scientometric analysis. There are far more literatures about SC than RC, and very few papers discuss SC and RC together. The research trend, category, and hotspots from research clusters are illustrated and compared. Major differences are discussed from their objectives, driving force, current research focus, and criticism. The literatures both related to SC and RC are used to explore their connections, which are very limited. The results revealed that the RC's impact on SC are positive from physical, social, and environmental aspects, while SC's impacts on RC could be both positive and negative from the above three aspects. It is indicated that SC and RC are both important for urban planning and can be complementary to each other through proper design and governance, which implies the need for building a resilient smart city (RSC). This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Visualization
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".