The Economy Behind Tourism: An Input-Output Approach in Measuring Contribution
Bibliographic record
Abstract
Central Java Province has the potential with a high number of tourist attractions, however, it has not provided optimal contribution yet to the tourism sector.This research aims to analyze the relationship between the economic sectors, the distribution impact, and the multiplier effect of output, income and labor in tourism sector on the economy in Central Java Province.This research uses secondary data, the Input-Output Table of Central Java Province, published in 2021, which is then analyzed using the Input-Output method.The result of the research indicates that the tourism sector for forward linkage is in the third highest position, and the highest supporting sub-sector of the tourism sector is the information and communication services sub-sector.Meanwhile, for the result of the backward linkage value, the tourism sector is in the third highest position, and its supporting sub-sector with the highest value is the accommodation and food and beverage provision sub-sector.The analysis result of the distribution impact indicates that the tourism sector is a leading sector.For the multiplier effect of output, the tourism sector is in the third highest position, while for the multiplier of income and labor, it is not too high in the sixth and fifth positions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".