Bibliographic record
Abstract
The purpose of this study is to estimate the effects of economic factors on the demand for luxury hotel rooms in the United States during the 16-year period . The average daily rate of six types of hotel rooms, gross domestic product and two recessions are considered as independent variables in the sample of the time series data set of 192 points to predict luxury room night stays of customers by ex-post data. Autoregressive Distributed Lag Model is employed to select the best model of luxury hotel demand on its determinants in the short and long run relationships. Findings indicate that in the long run, (1) the US residents would stay more nights in luxury hotels when their income increases; (2) the Canadian and UK might not visit or stay in the luxury hotels in the U.S. when their income or luxury hotel price increases; and (3) the German, Japanese, Korean and Chinese visitors would stay in the luxury hotels in the U.S. when their incomes increase no matter what the luxury hotel price increases. In the short run, the Chinese, Japanese, and Korean might not stay in the luxury hotels in the U.S. when their income or hotel price increases. The English would stay in the luxury hotels when their income or luxury hotel price increases. Finally, the two US economy recessions in 2001 and 2007-2009 do not affect the demand for luxury hotel rooms in the long run
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".