Tourism Demand Spillovers between Australia and New Zealand
Bibliographic record
Abstract
International tourism is susceptible to fluctuations and shocks. The spillovers of international inbound tourism between Australia and New Zealand have been one of the key issues for both governments and tourism authorities to address. This paper used a bivariate GARCH model to investigate the spillovers of international tourist arrivals between Australia and New Zealand from seven countries (Canada, China, Germany, Japan, Korea, United Kingdom, and United States). The monthly international tourist arrivals between 2000 and 2012 were used for the empirical analysis. The findings suggested a significant spillover of Chinese and Japanese tourists from New Zealand to Australia, whereas New Zealand’s tourism demand from China and Japan was not significantly affected by that of Australia. However, New Zealand’s inbound tourism from Canada, Germany, and United States was significantly affected by tourism demand from those countries to Australia. Furthermore, symmetric spillovers between Australia and New Zealand (in both directions) existed for UK tourists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".