Standardization of the Sites of Tourist Destinations in Ukraine as a Means of Implementation of the Internet Technologies on the Local Territorial Level of the Tourist Area
Bibliographic record
Abstract
In this article the single approach to standardization of the sites of the touristic destinations in Ukraine was offered as a means of implementation of the Internet-technologies on the local territorial level of the touristic area. It was proved that the rapid development of the innovation technologies led to the necessity to bring the touristic sites in compliance with the world standards (requirements). It should be implemented on the level of standardization of structure of the touristic sites. Such situation will lead to increase of the competitive ability of the touristic branch of Ukraine on the international market and distribution of the information about Ukraine in the global scales. The author forms the single approach to application of the certain group of technologies on the level of all destinations, which form the spatial framework of the innovative system of the touristic site in Ukraine. The implementation of the offered standardized structure allows touristic destination of Ukraine providing touristic product with assistance of the innovative technologies and enhances its competitive ability in the area of tourism. The integration to the systems of reservation of the basic services and social networks Facebook, and Twitter will promote to enhancement of the quantity of the foreign tourists in Ukraine.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".