COVID-19 Pandemic Strategies in Tourism Activity as Guidelines for Ex-Yugoslavia Countries Tourism Recovery
Bibliographic record
Abstract
Tourism economy is severely affected by COVID-19 pandemic, and if it is not adequately handled, the industry will suffer further negative consequences, resulting in economic failure. This study attempts to formulate post-COVID-19 recovery strategies for Destination Management Organizations (DMO) in six ex-Yugoslavia (Ex-Yu) countries. In order to achieve this, firstly, an overview of popularity of tourist sights in the Ex-Yu is given, benchmarking pre-COVID-19 to the current pandemic scenario; secondly, global best case practices of post-COVID-19 recovery strategies in Tourism economy are analyzed, drawing paralles with Ex-Yu countries. To understand the effect of the global pandemic on the international tourism and in Ex-Yu countries, statistical data from reputable and authentic data sources was collected and analyzed. The research findings prove that effective, long-term strategies are necessary to recover the industry from the negative effects of the pandemic. This pandemic has left such an impact on this industry, that it will be a challenge to overcome the consequences, some of them for years to stay. Therefore, governments and international organisations, as well as private companies, must establish a long-term plan for the industry so that it does not fail again, as it did in this case, in order to continue on the path of growth. At various stages, methods should be consistent and complementary. Thus, recovery strategies need to respond to challenges in a way that ensures a planned and effective recovery of the industry.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".