Theoretic and Methodological Bases for the Organization of Ethnic Tours for Children
Bibliographic record
Abstract
Ethnic tourism is one of the trends of tourism with its own distinguished ethnical, language and cultural components. The purpose of this type of tourism is to get acquainted with the way of life, culture, traditions and customs of the people who live in harmony with the natural environment. The Republic Mari El is a region where many nationalities and ethnic groups live in friendship and consent. But the nation, entitled to this territory, remains the Mari, who, regardless of the assimilation process, managed to preserve their own language, traditional folk clothes, their customs and traditions, their philosophy and religion, folk songs and dance. When organizing an ethnic tour, the content of the tour and the form of the organization of the children’s activity should be clearly defined. This also involves defining the level of the students’ awareness of the aspects of history and culture, as well as biography and geography. The acquired results of the survey helped to specify the content of ethnic tours for children in the Republic Mari El. As a form of organization of ethnic tourism we suggest visit in our Republic’s cities and districts, organizing game program sand trainings, as well as meetings with the famous folk craftsmen, participating in folk feasts, etc. The final form of ethnic tourism activity is a research and practice conference, where the students present their own research projects.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".