Bibliographic record
Abstract
Scholars have confirmed that tourists' intentions reflect actual purchases in consumer decision models. The difference between tourism planned behaviors and actual behaviors has been discussed in North America through 1) a study probing tourists who traveled to Alachuca County in Florida; and 2) Europeans and North American tourists who have traveled to Prince Edward Island in Canada. However, in Asia, the gap between planned behavior and actual behavior is still unknown. Therefore, this research tries to understand the relationship between planned behaviors and realized behaviors in Taiwan. In addition, novelty-seeking and their past behaviors such as the frequency of trips to Taiwan are also explored. The survey instruments were based upon various tourism marketing researches. The questionnaire was conducted with a convenience sample of 393 Japan tourists between February 2 and May 15. 2008. The research findings are as follows: 1. In terms of the number of indulged travel activities, realized tourism behaviors are greater than planned ones; 2. The stronger Japanese tourists' novelty-seeking reflects that the greater differences exist between their planned behaviors and realized behavior; and 3. The past behavior doesn’t influence the gap existing between planned behaviors and realized behaviors. Implications for future research and marketing strategies for relevant tourism industries and government agents are also discussed.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".