Bibliographic record
Abstract
Festival culture is an important carrier of the emotion and spirit of a nation. The study of holiday culture is an indispensable part of the study of a national culture. Since both American culture and Chinese culture have the characteristics of diversity and pluralism, a comparative study of them is meaningful. This paper studies some of the major festivals in both China and the United States of America, compares the similarities and the differences in the festival practices and trys to reveal the national personality, cultural value orientation, and the national spirit of them for the purpose of a better understanding and harmonious developmeng of the two cultures. Keywords: festival culture; comparative study; Sino-USResume: La culture festival est un vecteur important de l'emotion et l'esprit d'une nation. L'etude de la culture de vacances est un element indispensable de l'etude d'une culture nationale. Comme la culture americaine et la culture chinoises ont toutes les deux les caracteristiques de la diversite et du pluralisme, une etude comparative d'entre eux est significative. Cet article etudie plusieurs grands festivaux en Chine et aux Etats-Unis, compare les similitudes et les differences dans les pratiques de festival et essaie de reveler la personnalite nationale, l'orientation des valeurs culturelles et l'esprit national pour une meilleure comprehension et un developpement harmonieux de ces deux cultures.Mots-cles: culture festival; etude comparative; sino-americain
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".