The Contribution of the Tourism Sector to the Regional Spatial Economy During the COVID-19 Pandemic
Why this work is in the frame
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Bibliographic record
Abstract
The COVID-19 pandemic is causing significant global changes, and one of the most affected sectors is the tourism industry. Therefore, this study aimed at determining the impacts of the pandemic on tourism by comparing the spatial economic classification before and during the pandemic using four analyses, namely the regional Klassen Typology, sector approach, the Location Quotient (LQ), and Shift-Share Analysis. The processed information is secondary data from the Central Statistical Agency of Karo Regency and North Sumatra Province, Indonesia. This area is one of the mainstay areas of the national economy and part of the Lake Toba Super Priority Destination. The obtained results showed that the tourism sector was classified among the fast-growing and uncompetitive sectors before the pandemic. However, during the crisis, it became a slow-growing and competitive sector.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it