Bibliographic record
Abstract
Accommodation plays a significant role in tourism infrastructure; without a reliable place to stay overnight, tourists will not visit the location. Aligning with concepts described in the 2008 Tourism Satellite Account: Recommended Methodological Framework (TSA: RMF 2008), accommodation services are among 12 tourism sectors which are interrelated with each other through flow-on activities. When accommodation establishments purchase goods and services through their day-to-day transaction and pay their workers, this money is recirculated through the economy. Therefore, this study examined the impacts of expenditure spent by the visitors for accommodation on the economy both directly and indirectly. The Leontief Input-Output Model for the twelve tourism sectors was developed to calculate the impacts of tourist spending on lodging and other interrelated tourism services in Thailand. Data used in this study are from both primary and secondary sources. Findings indicated that tourism accommodation in Thailand was the second largest contributor to the tourism industry after “Air passenger transport services”. The computed multiplier showed that linkages of services between accommodation services providers and other tourism sectors helped generate income for the economy higher than the direct income paid directly from the tourists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".