Domestic Tourism in Municipalities of the Northwestern Federal District: Statistical Assessments and Impact of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Currently, in the geography of tourism, a research field is rapidly developing that studies the dynamics and direction of tourist flows, based on official statistics at the level of states, regions, and lower administrative units. In 2022, the Federal State Statistics Service of Russia for the first time provided statistics on arrivals and overnight stays in municipalities of the country, which allowed the authors to classify them according to these indicators for 2021 in the Northwestern Federal District. The classification of municipalities was based on six indicators characterizing the volume of tourist flow and degree of development of the hotel and restaurant infrastructure. Due to the COVID-19 pandemic, the Northwestern Federal District experienced a significant reduction in tourist flow volume in 2020. However, with a sevenfold decrease in inbound tourist flow, the domestic tourist flow decreased only by a quarter. As well, already in 2021, in the federal district, the domestic tourist flow increased by almost 1.5 times, which allowed it to replace the decrease in the inbound tourist flow in the first year of the pandemic by more than a third. The cartographic analysis accompanying the development of the classification of municipal districts made it possible to spot intraregional differences in the size of the tourist flow and development of tourist infrastructure unseen when analyzing tourism statistics at the regional level. Thus, the study revealed a number of tourist anomalies within regions, when the volume of tourist flow does not correspond to the degree of development of the existing hotel and restaurant infrastructure. The results of the study can be used in planning the development of tourism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".