TERRITORY MARKETING: STUDY OF KEY TRENDS IN THE SCIENTIFIC RESEARCH DEVELOPMENT
Bibliographic record
Abstract
Territorial marketing is a multifaceted scientific direction that includes elements of branding, use of digital technologies, performance evaluation and consideration of cultural factors. It is an important tool for territories' economic, social, cultural and ecological development. It helps attract investment, support local businesses, improve the quality of life for residents, preserve cultural heritage and promote sustainable development. The purpose of the article is to study the main trends in publishing activity on the subject of territory marketing. The authors have systematised the research areas of territory marketing: the conceptual foundations of territory marketing, territory branding strategies, the use of digital technologies in territory marketing, measuring the effectiveness of marketing campaigns, and the influence of cultural and social factors. With the help of the Scopus tools, the peculiarities of publication activity on territory marketing were investigated. For the period from 1990 to 2023, more than 1,573 publications were analysed. Italy, the USA, France, Spain, Great Britain, Australia, Portugal, India, and Canada are among the leading countries in publishing activity. Key subject areas include Social Sciences, Business, Management and Accounting, Economics, Econometrics and Finance, Environmental Science, Engineering, and Medicine. The most significant publications are affiliated with the Polytechnic Institute of Cávado and Ave, Portugal, the University of Guam, Guam Island, and Charles Darwin University, Australia. The authors conclude that socio-economic development and territorial development strategies focus on increasing competitiveness by preserving and using existing, as well as forming and implementing new competitive advantages, which determines the positioning of the territory. Given this, the relevant strategies for developing territories and cities should ensure the convergence of management and economic activities of the subjects of the territorial system, effective management of production infrastructure, and provision of consumer needs. In the face of global environmental challenges, marketing territories can contribute to promoting environmentally friendly technologies and practices. This helps to attract investments in sustainable projects and increase the population's environmental awareness.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.068 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".