Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The forecasting of international tourist arrivals is normally done on a per country basis. The volume of tourist flow varies widely among countries, largely depending on their population size but also on their openness to tourism. This paper uses data for Austria, China (PRC), Canada, the Cook Islands, Cyprus, Japan, the Maldives, Malta, New Zealand, Singapore, Slovenia and Thailand over a quarterly estimation period from 1995 to 1999 to forecast ahead for 2000 to 2002. In addition, the total arrivals to Japan from 24 different countries of origin are also examined with the same estimation and forecast periods. The topic explored is whether it is possible to examine the structure of the time-series data to determine why particular forecasting is more or less accurate. As a starting point, forecasts are obtained from larger data volumes relative to smaller volumes. The forecasting comparison uses the short-term time series methods of the basic structural model and the Holt–Winters model, with a comparison for base accuracy against the naïve model. The results show in the forecasting comparison that the volume of flow and volatility and seasonality do not directly influence the accuracy of the forecast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it