Challenges in the Tourism Industry During COVID-19 Pandemic in Kosovo
Bibliographic record
Abstract
From March 2020 to early June 2020, Kosovo introduced various travel restriction measures for foreign nationals, including entry bans, closure of land and air border crossings, two-week quarantine, and self-isolation. From March to May, the country was almost completely blocked, where the activities of the tourism industry were strictly prohibited while businesses encountered great difficulties to operate and provide services, the number of jobseekers in the country increased significantly, precisely because of the cessation of activities, because this sector is considered to be one of the sectors that contributes the most to employment in our country, despite the fact that some of these businesses are family businesses. A significant number of employees in these sectors in our country may lose their jobs, as a result of the situation created, and this will further deepen the tourism industry, mainly due to the decline in the volume of remittances and tourism diaspora in Kosovo. While restrictive measures may have had a positive impact on curbing the spread of the virus, and preventing the escalation of the health crisis, the effect and consequences of closing the economy for months is severe for the tourism industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".