MARKETING APPROACH TO ASSESSMENT AND MANAGEMENT OF BUSINESS ATTRACTIVENESS OF TERRITORIES
Bibliographic record
Abstract
The marketing approach to the assessment and management of the business attractiveness of territories is considered. It was determined that the initial condition for the implementation of the marketing approach to business attractiveness is the need to study, analyze and forecast the needs of the business audience and develop a system of measures to support, increase or decrease it within the framework of regional policy. A group of basic partial statistical indicators and group indices has been formed, which are proposed to be considered as indicators of business attractiveness. It is also proposed to use an expert assessment of the attractiveness of the region based on subjective-objective perceptions, a feature of this approach is to focus attention on the detailing of individual criteria that reveal the attractiveness of the territory in terms of satisfying the interests of defined groups of target audiences. The modern experience of forming the business attractiveness of European countries was systematized, which made it possible to highlight the experience of Poland, which demonstrates the multi-vector attractiveness of its regions; the successful practice of promoting the region to the target audience "external business", demonstrating North Limburg, which five hundred international companies have chosen as the basis for their pan-European operations; the experience of formation of territorial marketing through such organizational forms as promotion agencies of Canada, China, Slovakia, Spain, Palestine, India was studied. The problems that exist in the regions of Ukraine and require a national solution are identified (protection of property rights, permits for temporary stay in the country, highly bureaucratized procedures for obtaining a work permit, difficulties with opening accounts for non-residents and providing administrative services, currency restrictions, the inability to normally repatriate capital).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".