Financial Regulatory Intervention in Encouraging the Tourism Industry-Portrait of the Top 5 Popular Destinations in Indonesia
Bibliographic record
Abstract
Throughout the end of 2019 to 2021, COVID-19 had stolen the attention of many parties.Besides the eliminating crowds through a regional-national locking system, this incident also poses a threat to the world's population habitat, causes economic suffering, fades individual psychology, ethnic divisions, and makes other multi-components critical (such as the sustainability of tourist destinations).Although the government's efforts in many countries in reorganizing the tourism ecosystem are seen as not yet concrete, they should at least inform the public that there are positive initiatives to restore visitor confidence.Learning from this case, the motive of this paper is to investigate the determination of the National Economic Recovery (PEN) program which has been distributed by the Indonesian government since 2019 to encourage the informal sector such as the tourism industry.The data is divided into six key variables, which are grouped into two components.The observations are concerned with the top-5 destinations from Indonesia.After that, the data were calculated into three patterns (normal, post-pandemic, and towards endemic) and analysed using a linear regression approach.A series of explorations concludes in three equal methods, where the first and third models show that the PEN program has a significant effect on Tourism Visit Volume (TVV).The study also confirmed that the two variables were also significantly related in the second model, despite the decline in the PEN budget.This finding focuses on two alternative schemes.The first urgency highlights practical regulations in preventing the effects of a pandemic that has the potential to darken the existence of tourism.Second, empirical evaluation teaches logical handling from an academic perspective for the advancement of tourist destinations in the future.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".