Industrial Life-Cycle and the Development of the Russian Tourism Industry
Bibliographic record
Abstract
The purpose of the study presented in the paper is to highlight the influence of the microeconomic factors related to the evolutionary stage of the industry’s life cycle on the industry dynamics. The authors use the example of the Russian tourism industry to show that microeconomic factors are important, along with the macroeconomic, market, and demand characteristics external to the industry. Data mining was applied to obtain data from the industrial enterprise database and Rostourism official documents since there are no regular Russian statistics on firms’ exit and new entry. The authors used annual ranked listing of firms by their revenues to determine the structural indicators of the industry. The results confirm that it is important to consider not only the demand and macroeconomic indicators, which are external risks in relation to the industry, but also the internal processes at the different stages of the product cycle. In a sufficiently long period, the influence of microeconomic indicators may be no less strong than the business factors of financial risk. One should take this into consideration in econometric modeling on long time-series.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".