Marketing of Cities as Centres of Socio-Economic Development in the Process of Globalisation
Bibliographic record
Abstract
Modern trends in the formation of urban development are based primarily on the extent to which the territory of a city can be attractive for investors, interested people in general and public administration systems. When regarding programs for the development of urban areas in the structure of sustainable development, it is necessary to highlight the methods of forming an attractive image of a city, which is considered a marketing tool. Understanding the possibilities for the development of an urban area requires the formation of tools for spatial marketing and opportunities for the establishment of measures for the development of individual tools of communication between city authorities and the external environment. The novelty of the research is determined by the structural feature of the formation of an integrated method of using marketing tools to promote the image of a city in the informational, social, and cultural aspects. The authors show the tools for implementing the marketing strategy of the urban area as elements of sustainable development. Stakeholders of sustainable development are shown not only local management structures but also global investment funds and transnational corporations. The practical significance of the study is determined by the possibility of forming based on an urban area, which is marked by the presence of sustainable development markers, innovative and science-intensive centres, and analytical corporations. The formation of development centres is also possible through the creation of smart cities.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".