Status and trends of tourism development in the light of the results of studies on the competitiveness of the districts of Podkarpacie Province
Bibliographic record
Abstract
Th e competitiveness of the tourist reception areas is the ability to achieve greater economic, social and cultural eff ects related to the development of tourism than the average for a country or a selected area across a continent or across the world. Regions compete with each other for both tourists and investors, also outside the tourist industry. In the era of globalization, competition between regions also increases its spatial extent. Moreover, it is diffi cult to talk about the ability to compete without having a vision of the future or having appropriate tools for the implementation of the vision, but these are just the beginning, and the eff ects which can bring tangible benefi ts to a region are the fruits of skillfully and consistently pursued policies in the development of each tourist region such as Podkarpacie Province with a great number of its competing districts. Th e aim of this article is to analyse the major determinants of tourist competitiveness related to the new paradigm of regional development, based on the example of Podkarpacie districts. Th e competitiveness of the tourist districts in Podkarpacie Province depends largely on their tourist attractiveness and their attractiveness for investors. On the basis of studies1 on the competitiveness of Podkrpackie districts, presented in the article, the status and trends of tourism development in this area are analysed. JEL Classifi cation Code: L23
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".