IMPLEMENTATION MODELS FOR THE "SMART CITY" CONCEPT IN THE STRATEGIES FOR SOCIO-ECONOMIC DEVELOPMENT OF LARGE CITIES IN THE RUSSIAN FEDERATION
Bibliographic record
Abstract
The concept of "smart city" is one of the most popular in the practice of urban management around the world. It has been adopted at the federal management level as one of the leading ones for Russian cities. The problem lies in the objectification of the concept in the strategies of socio-economic development of cities. In addition, each concept, including the "smart city" has a variety of implementation models. There are technocratic (rating), triple, quarter spiral, etc. The purpose of the work is to determine the types of models for the implementation of the "smart city" concept in the strategies of socio-economic development of large Russian cities. The focus of the research is the model of the quarter spiral, which received its author's continuation and refinement as a subject through the strengthening of the nature of its sociality. To achieve this goal, a content analysis of the strategies of socio-economic development of cities in 20 major cities of the Russian Federation was carried out. As a result, it can be argued that the concept of a "smart city" is not represented in all strategies of large cities. Where it is stated, the prevailing type of model for the implementation of the concept is the triple helix model (consumer model). The presence of elements of the subject model in strategic documents seems promising.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".