A Comparative Analysis of Seven Smart City Development Projects: Institutional, Economic, Technical, and Policy Perspectives
Bibliographic record
Abstract
This paper argues for the use of a multifaceted, and contextualized approach to smart city development by unpacking how individual smart city initiatives have planned and implemented diverse projects based on their distinct environments, stakeholders, and goals. We evaluated and compared the institutional, economic, technical and policy characteristics of seven smart city initiatives (Montgomery, San Diego, New York City, Calgary, London, Vienna, Singapore). Our findings demonstrate three principal implications in smart city development. First, the surveyed smart cities established concrete cases for the use of different project development models in terms of leadership and governance styles, adoption of smart city applications, and planning and management strategies. Second, such differences stemmed from the multifaceted interactions that link environment, stakeholders, and goals. Finally, knowledge management (KM) played a crucial role in ensuring the accumulation and transferability of organizational and policymaking infrastructure within and between smart city initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".