Socio-Cultural Sphere, Governance and City Branding in New Economy
Bibliographic record
Abstract
The article analyzes the problems of socio-cultural sphere in the new economy, as well as the impact of socio-cultural sphere on the brand of St. Petersburg, paper also discusses possibility of evaluation of changes in brand strength due to socio-cultural impact. The proposed method of estimation of the strength of the brand is universal enough, but due to a pronounced specificity of various segments of the public sector it requires specifying when evaluating consumer interest in services for various types of organizations. In this paper, brand evaluation can not be based on the financial performance of organizations; we are not interested on the characteristics of brand equity, but on indicators of consumer preferences and the associated strength of the brand on the market. The proposed method can be adapted to different segments of the public sector (and their impact on brand site) that require adaptation indicators of demand and availability of the services of these organizations. It requires considering not only the classical estimation of the brand, calculated using the method of The Boston Consulting Group (BCG) including adaptation of its indicators for socio-cultural sphere, and together with the emotional evaluation of brand strength by Keller (Keller, 2005) used to calculate the integral market power of the brand. Priority of emotional components is defined by specific traits of Russian market in general and the cultural market, in particular. The competitiveness of the regional institutions of socio-cultural sector and the strength of their brand directly affects the competitiveness of the region as such. The paper raises questions of so called “star” status and orientation on brands in risk conditions. Brand allows city to start the process of urban regeneration, become the basis of its development strategy, and to revive the use of vacant spaces as well. Branded cities attract not only tourists but also entrepreneurs in various industries, people who want to live interesting, in an unique location and have access to the benefits of modern goods, unique heritage, objects and experiences. Brand adds a certain “flavour” to the city. It can be promoted; it can attract the most qualified workers not only with competitive salary and benefit packages, but with a good place for work and for pleasures as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".