Analyzing the Behavioral Trends in Tourist Arrivals from Japan to Australia
Bibliographic record
Abstract
As tourism forecasts are obtained based on past observations, an historical analysis of Japan’s postwar economic success, social factors, and the national government’s institutional policies and reforms can help to provide a better understanding of the growth in Japanese outbound travel and the trending patterns in Japanese tourist arrivals to Australia. To achieve these aims, a statistical analysis of the time series behavior of tourism demand, specifically quarterly tourist arrivals from Japan to Australia from 1976 to 2000, are examined. In addition to analyzing the full sample, the authors also consider three subsamples, namely quarter 1 of 1976 to quarter 2 of 1987, quarter 3 of 1987 to quarter 2 of 1997, and quarter 3 of 1997 to quarter 2 of 2000, to evaluate the sensitivity of the estimates to changes in trends arising from the 1987 stock market crash and the Asian economic and financial crises in 1997. Autoregressive moving average time series models are estimated to analyze alternative patterns of trending behavior within this class of models.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".