Efficiency in Urban Management and Smart City Concepts: A Russian Cities Case Study
Bibliographic record
Abstract
In the modern context, for a city to be deemed smart, it must extend its focus beyond a mere technological infrastructure to align with the 4T model.This framework encompasses four key components: Technology, Telecommunication, Talent, and Tolerance.The model advocates for a comprehensive approach that transcends purely technological progress, emphasizing that a smart city's successful evolution requires advanced infrastructure, effective communication networks, skilled human capital, and a culture of inclusivity and tolerance.The article presents an analysis of applying the smart city concept and the 4T model in the development of Moscow, Krasnodar, Kazan, Tyumen, Yekaterinburg, and Dubna (Russia).The study aims to determine the application of the smart city and 4T model by their administrations and the difficulties in implementing these strategies.The study is conducted in five Russian cities varying in size and functionality.The research materials are documents describing the development strategy.The authors of the article conduct computer-assisted telephone interviews with residents and city administration employees.They analyze the development strategies of these cities.The authors also identify the positions of the 4T model and directions for the development of a smart city in local development policy.The analysis allows the authors to determine the maturity of local development planning mechanisms considering the 4T model and smart city tools.Since the experience of the five cities different in size and functionality is studied, the results might be of interest to both practitioners and policy theorists striving to involve residents in the formation of local development policies and use of modern technologies to improve their access to city services and increase their influence on the future of their city.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".