Financial Support of Tour Operator Activities: Issues of Implementation in the Russian Federation
Bibliographic record
Abstract
Studying the issues of implementation of financial support of tour operator activities in the Russian Federation, which have so far impeded the guaranteeing of protection of rights and legitimate interests of Russian tourists at a proper level, allows to formulate practical recommendations and suggestions for improvement of Russian legislation in order to enhance the efficiency of legal regulation of tourism field and protection of rights and legitimate interests of Russian citizens. The article presents a comparative study of the Russian financial guarantee institution and its foreign analogues, defines the causes of emergence of the institution of financial support of tour operator activities in Russian legislation and imposition of a prohibition on tour operator activities. We have come to the conclusion that acknowledgment of financial support of tour operator activities as financial guarantees of tour operator’s liability. Analysis of current Russian legislation allowed to detect its contradictions and develop practical recommendations for improvement of Russian tourism legislation. In particular, it allowed to draw a conclusion of the need to eliminate the non-conformance of standards of article 17.6 of the Federal law NO 132-FZ “On foundations of tourist activity in the Russian Federation” (“Tourist activity law” further on) issued on 24.11.1996 to the standards of item 1 of article 48; item 1 of article 53; item 1, item 3 of article 56; article 402 of the Civil code of the Russian Federation (part one); federal law â 51-FZ issued on 30.11.1994 (the Civil code of the Russian Federation).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".