ANALYSIS OF THE IMPACT OF THE PANDEMIC ON SEASIDE TOURISM IN ROMANIA AND LEBANON
Bibliographic record
Abstract
The hospitality industry has been severely affected by the health crisis. The World Tourism Organization recently indicated that the decrease in the volume of international tourism, in terms of the number of arrivals and receipts, respectively, was 74% compared to 2019. The year 2020 meant a loss of 1 billion international tourists and 1.3 trillion dollars, compared to 2019, a collapse about 11 times more drastic than in the case of the 2009 crisis. This dramatic decline was also reflected in the case of seaside tourism, internationally.Our paper tries to provide an image on the effects of the pandemic on coastal tourism in Romania and Lebanon, based on existing statistical data in the two countries. Thus, the analysis of the databases provided by the National Institute of Statistics in Romania shows that the occupancy rate for August - the month with the best occupancy rate on the coast - decreased by 23.91% in 2020 compared to from the previous year.In Lebanon, according to the reference survey by Economena Analytics, Ernst and Young, the third quarter recorded the highest occupancy rate of the year in the coastal area reaching the maximum value in 2019 of 78.5%. The same quarter recorded the lowest value of 25.3% in 2020. The seaside tourism industry was severely affected in both countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".