The Effect of Number of Visitors, Tourist Destinations, Hotel Room Tax and Accommodations on Original Local Government Revenue: Case Study West Sumatra Province, Indonesia
Bibliographic record
Abstract
This research aims to discover 1) The effect of number of domestic visitors on Original Local Government Revenue (OLGR) 2) The effect of number of foreign visitors on OLGR 3) The effect of number of tourist destinations on OLGR 4) The effect of restaurant tax on OLGR 5) The effect of hotel room tax on OLGR 6) Number of accommodations as a moderating variable for relationship hotel room tax and OLGR. The study population consisted of 12 regencies and 7 municipalities. The sampling technique uses purposive sampling. The selected sample is considered the most appropriate to represent tourism according to Tourism Office of West Sumatra Province. The selected sample is 3 municipalities and 2 regencies. Data source obtain from Central Bureau of Statistics (BPS) West Sumatra Province. Data analysis consisted of statistical descriptive analysis, model estimation test, classical assumption test, coefficient of determination test, F-test and t-test. The results show 1) The number of domestic visitors has a positive and significant effect on OLGR 2) The number of foreign visitors has a positive and significant effect on OLGR 3) The number of tourist destinations has a positive and significant effect on OLGR 4) Restaurant tax has a positive and significant effect on OLGR 5) Hotel room tax has a positive and significant effect on OLGR 6) Number of accommodations show evidence as a moderating variable for relationship hotel room tax and OLGR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".