Perpetuating Guan Gong Culture: A Design of Bilingual Wechat Service Platform for Jingzhou Guan Gong Yi Yuan
Bibliographic record
Abstract
Jingzhou Guan Gong Yi Yuan, as a landmark of local tourism culture, built in 2016, is located in Jingzhou, Hubei, with a total area of about 152,000 square meters. It opens a window to local traditional culture. In recent years, Guan Gong culture has enjoyed scholars’ great concern, but most studies merely related Guan Gong’s life story to its cultural images as the God of Wealth, Martial Arts, etc. few research suggest to facilitate and accelerate bilingual travel service of Guan Gong culture. In light of its urgent need for the public transmission, this article aims to make a creative design of a Chinese-English bilingual Wechat platform for the promotion of Guan Gong Culture. Results show that the layout of Guan Gong Yi Yuan sightseeing service platform can be divided into four sections: the first one is booking tickets by which visitors can directly consult and book tickets on the official account instead of sparing time on queuing and fetching tickets at the ticket window; the second menu is a map guided navigation by which visitors can follow the route guide and make a free choice of their favorite scenic spots; the third function is to involve Guan’s spirit and culture in the reality show; the fourth is a bilingual explanation of Guan Gong’s life and deeds together with his loyalty, folk images, etc.. By means of this Wechat platform, visitors may have a bird view and comprehensive and profound understanding of Guan Gong Culture. It is contributed to spread and internalize the essence of Guan Gong culture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".