Bibliographic record
Abstract
Based on a sample survey of 702 Guangzhou residents from October 2001 to January 2002, and outbound travel data collected from Guangzhou Travel Agencies, the author analyzes the tourist behavior of outbound travel for Guangzhou residents in different aspects such as time variation, tourism purpose, organizing styles, destination choice behavior in the paper. Finally, the author sums up the following rules of tourist behavior of outbound travel for Guangzhou residents. Outbound travel will become a hotspot for Guangzhou residents as the total number of Guangzhou outbound visitors has been rising quickly. The main market of outbound travel for Guangzhou residents is distributed in Hong Kong SAR and Macao SAR, the main markets of travel abroad in Guangzhou are distributed in the Southeast Asian countries, South Korea and Japan, indicating the number of the travelers is declining as the distance becomes longer.Some 53% of Guangzhou residents choose to travel within a distance less than 300 km and 87% within a distance less than 2500 km from Guangzhou. The first tourism purpose of Guangzhou residents for outbound travel is sightseeing, but the second tourism purpose to Hong Kong SAR, Macao SAR and Taiwan is visiting relatives and friends there, which is obviously different from traveling to foreign countries, while traveling to foreign countries is for vacation and recreation purpose. The tourism organizing mode of Guangzhou residents to Hong Kong SAR, Macao SAR and Taiwan is different from to foreign countries. The main organizing mode to Hong Kong SAR, Macao SAR and Taiwan is by themselves, the second mode is by travel agencies, but the main tourism organizing mode to foreign countries is by travel agencies, the second mode is by themselves. Other tourism organizing mode of Guangzhou residents for outbound travel is not obviously different. There was a distinguished difference between destination choice behavior and attitude for Guangzhou residents. The destination choice attitude is higher than the destination choice behavior except traveling to some Southeast Asian countries. According to the visited-rate of Guangzhou residents, the top eight regions and countries are Hong Kong SAR(39%), Macao SAR(21.1%), Thailand(17.5%), Singapore(10.8%), Malaysia (8.7%), United States of America (5.4%), Vietnam (4.6%) and Japan (3.8%) and The top eight countries for expecting visited-rate of Guangzhou residents is France (22.5%), United States of America (21.8%), Australia (19.9%), Japan (16.8%), Singapore (12.1%),United Kingdom(11%), Canada (8.5%) and Thailand(7.3%).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".