Bibliographic record
Abstract
Lost Horizon (Hilton) is a captivating novel about a remote idyllic place called Shangri-La, located somewhere in the mountains of Tibet. This fabled place is a wonderland where man and nature coexist in harmony as do several ethnic groups. It is a land of eternally young inhabitants blessed with magnificent landscapes shrouded in mysticism. The fantasy of this mythical place has fueled a search that has yielded many Shangri-Las in the Himalayas, each attempting to fulfill Hilton’s vision. Nevertheless, only one of them, the county of Zhongdian in China’s Yunnan Province, has been granted the use of Shangri-La brand name by the People’s Republic of China (PRC) State Council. In the present study, the researchers find that the reshaping of Zhongdian into the enchanted land of Shangri-La relies upon processes of sacralization, ethnitization, and exoticization. This research explores the construction and marketization of this mythical creation and its cultural, economic, social, and environmental consequences. This analysis also distinguishes the contrasting aims of Han Chinese and Western tourists when traveling to this encapsulated paradise.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".