Tourism, accommodation, and the regional economy in Indonesia’s West Papua
Bibliographic record
Abstract
This study deals with the contribution of visitor expenditure on West Papua’s regional economy. It accomplishes three objectives: (1) to estimate the economic contribution of domestic and inbound visitor expenditure; (2) to measure the economic contribution of tourist spending at various accommodation classes; and (3) to describe the use of local commodities and labor in the regional accommodation industry. To accomplish the first and second objectives, an input-output multiplier analysis was employed. As for the third objective, interviews were conducted with 35 representatives from regional accommodation establishments. Tourism is found to contribute greatly to the regional economy, as shown from the higher overall output multiplier for tourist expenditure as compared to the regional output multiplier. The output multiplier for inbound tourist expenditure is higher than the domestic tourist. Three-star accommodations are found to be the biggest contributor with outstanding inter-sectoral impact on fisheries; food, beverage, and tobacco manufacture; and agriculture. The qualitative analysis suggests the existence of a large leakage (±90%), mainly in produce and chemicals used in daily operations. Fisheries and wood furniture are the exception. Overall, the accommodation sector absorbs a considerable extent of local labor (73%), 23% of which are Indigenous Papuans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".