The Geographical Space of China's Tourism Websites
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
Abstract A new special space is being constructed from information and user flows based on tourism websites and the links among them. While greater attention has been paid to the economic, technological and user behaviour aspects, tourism websites also deserve to be studied from the perspective of space using geographical methods. This paper is a cybergeographical study of tourism websites. Based upon related studies and theories of cyberspace, the new topic 'tourism cyberspace' is introduced. Then, using spatial network analysis methods, the complicated cyberspace of Chinese tourism websites (CTW) is represented through the construction of a network model based upon data collected from the CTW of 31 regions of China. The model is a simplified network displayed as a 31 × 31 matrix. In this network, 'nodes', 'accessibilities' and 'strengths' are calculated and estimated, and the results show significant regional differentiation. Furthermore, positive relationships are indicated between the level of the regional economic, tourism and technological development and the accessibility of the region's nodes. This suggests that there is a strong relationship between tourism cyberspace and traditional geographical space. Key Words: InternetcyberspaceChinese tourism websitesspatial analysis Acknowledgement The authors thank the graduate students of Professor Jie Zhang's group, Zehua Liu, Ying Yu, Tai Huang and Zhu Xie in particular, for their assistance with collecting the related data and conducting surveys. The whole project (Case Study on Empirical Models and Spatial Pattern of Tourist/Recreation Flows of China) was supported by the National Natural Science Foundation, China (project code: 40371030).
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 | 0.002 | 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 | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it