Innovations in mountain guide training for the tourism business in Almaty
Bibliographic record
Abstract
The relevance of the proposed project is due to three main factors.The first is the real level of unemployment, which, according to experts, is quite high in Kazakhstan. The second is the consequences of the COVID-19 pandemic and the third is the consequences of the events of January 2022 in Almaty. Unemployment in Almaty, according to official data for the first quarter of 2021, was 5.2% (according to Finprom.kz). But, in fact, this figure is much higher and concerns primarily young people. That is why our project is designed mainly for young people of student age studying in universities with a degree in Tourism. The purpose of the article is to show what kind of innovations should be used to train mountain guides, taking into account the socio-economic problems of Almaty. The development of domestic and inbound tourism has always been an important part of tourism business of any state, including in the Republic of Kazakhstan. At the same time, mountain tourism programs are relevant. In this regard, in the context of the COVID-19 pandemic, a shortage of qualified mountain guides has become apparent and aggravated. Also, often the services of a guide are performed by persons who do not have professional training to work with clients in the mountains. This fact is confirmed by periodic extreme situations, led by an unprepared guide. This article describes our innovative experience in training mountain guides for the tourism business in Almaty. This article describes our innovative experience in training mountain guides in tourism business in Almaty. The choice of training this particular category of specialists is due to the presence of a large amount of tourist and recreational resources for the development of mountain tourism near the city of Almaty. We believe that the mountain guide training project will be in high demand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".