The Image of Municipalities
Bibliographic record
Abstract
Purpose of the study: The aim of this study is to investigate the image of municipalities and also to determine the things that give the city a positive image of the territory. Methodology: The theoretical and methodological basis of the study is the ideas and paradigms that were developed in the works of leading domestic sociologists, cult urologists, economists, and marketers. The applied individual methods include comparative analysis, typology, classification method, questionnaire, method of analysis of social factors and events Results: The Program of socio-economic development must be amended in the form of a subprogram for the formation of a positive image of the municipality of Chekhov, since today there are no projects or programs aimed at creating a positive image of the municipality to one degree or another, its popularization and perception among the local population and among tourists. Moreover, information support of the main assets of the municipality of Chekhov (information about the advantages of the territory, photographs and advertising leaflets) in social networks, the media, on the Internet, that is, marketing of historical and cultural heritage with a priority on the work of A.P. Chekhov Applications of this study: The formation of a favorable image is of fundamental importance primarily for residents of the municipality. It affects the creation and support of social optimism among the population, trust in the authorities and confidence in the future. Novelty of the study: The image of the municipality is formed from the priority elements of its structure and a set of certain factors of development and promotion of the territory. So, the novelty of this study is to asses this image in modern life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".