The Role of Digital Geography in the Development of Tourism and Tourist Activities
Bibliographic record
Abstract
Geography and tourism are interconnected and mutually enriching areas. Tourism, as the most susceptible activity to innovations, responds to digital geography by increasing population flows, developing territories, organizing services and leads to an increase in the level of self-organization of activities. Digital technologies are actively penetrating the field of excursions and independent tourism. Mobile audio guides capture the infinite variety of urban space, help young people get involved in the process of developing audio guides, update knowledge of geography, history, culture and form the skill of digital presentation of the knowledge gained. Digital technologies contribute to the intensive development of independent tourism in places inside of the "Infrastructures oecumene ". High rates of inbound tourism are registered in Iceland. Tourism in the form of a family trip by car is largely due to the arrival of digital services for accommodation and meals, car rental. In connection with the development of digital geography in the sphere of tourism, new areas of research are taking shape. The main aim of the study is to investigate the role of digital geography in the development of tourism and tourist activities and attempt to draw some practical and innovative conclusion since the tourism industry has been one of the most lucrative industries over the last decades. In this study, methods of analysis and synthesis of scientific literature, data from a review of audio tours of Perm on the izi.TRAVEL platform, and official tourism statistics from Iceland were utilized to fulfil the aim of the study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".