Influence of Tourism Industry Development on the Regional Labour Market (on the Example of the Yaroslavl Region)
Bibliographic record
Abstract
The decrease of employment level in the region and priority of developing tourism industry as a strategic factor of unemployment rate decrease are estimated in the article based on research of the current condition of Yaroslavl region labour market. It was determined that conditions of labour market and tourism industry of the Yaroslavl region are connected as complementary and interdependent functional areas. It was defined that tourism sphere has a multiplied positive effect on the employment level in the region due to increase of its functioning scope caused by relatively high shares of unoccupied population in the region. The basic development trends of the tourism industry in the Yaroslavl region characterised by positive development of a tourist infrastructure, improved quality, and outreach of services range, increase of tourist inflow in the region were designated in the research. It was found out that availability of various tourist resources in the Yaroslavl region creates conditions for many kinds of tourism: cultural and informative, cruise, medical and secondary wellness, business and event, ecological and adventure, as well as rural tourism. Taking into account results of research of the main regional features and potential, groups of development potential of the tourism industry in the Yaroslavl region were determined. Such groups include natural and geographical, culture-historical, professional and demographic, social and economic, material and technical, and economic-political potentials. The main destabilising factors of development of the regional tourism market as the base for increase of employment level in the region were summarized, and the complex of conceptual recommendations for creation of favourable conditions for formation of development potential was proposed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".