Bibliographic record
Abstract
Since the tourism faith default of China’s tourism industry has become a fiercely harmful problem, strengthening the faith construction becomes an inevitable choice and the urgent issue. Seen from the economic angle, the tourism industry faith default in China is caused by three reasons. These are the asymmetrical information among the economic main bodies, limited gaming in this industry, and “the government failure”. Therefore, the tourism enterprises, the government, and tourism industry associations should joint efforts to develop the tourism faith construction and build a good faithful environment. Key words: Tourism Faith, Faith Default, Faith Construction Resume: Actuellement, un serieux probleme du manque de l’honnetete s' apparait dans le tourisme, c’est un certain choix et une obligation pressante d’intensifier l’edification de l’honnetete du tourisme en vue de realiser nos developpements dans ce domaine. Au point de vue d’economie, la cause principale de ce probleme est des informations asymetriques et des concurrences restreintes entre les parties principales d’economie dans le tourisme, ainsi que “le sans controle du gouvernement”. Il faut que les entreprise touristiques, le gouvernement, les associations touristiques travaillent ensemble de pratiquer la construction de l’honnetete du tourisme et de bâtir l’environnement honnete. Mots-Cles: l’honnetete du tourisme , le manque de l’honnetete , l’edification de l’honnetete
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".