Bibliographic record
Abstract
With the development of globalization, cross-culture communication is indispensable to an open society in which we live today. Therefore, cultural differences are everywhere. The cultures between the East and the West are distinguished by a rather large scale. It means not only the opinions or ways of thinking are different, but how do people behave in daily life is also not the same, sometimes may even the opposite. This paper will first probe into the causes for cultural differences and then some of the typical examples to illustrate the cultural difference between east and west, and finally, ways to fit in different cultures. Key words: culture, difference, east, west Resume: Avec le developpement de la globalisation, la communication transculturelle est indispensable pour une societe ouverte dans laquelle nous vivons aujourd’hui. Cependant, les differences culturelles se trouvent partout. Les cultures entre l’Est et l’Ouste sont distinguees dans une large mesure. Les facons de penser non seulement sont differentes, mais les comportements des gens dans la vie quotidienne ne sont pas les memes, parfois le contraire. L’article present examine d’abord les causes des differences culturelles, puis montre des exemples typiques pour illustrer la differences culturelle entre l’Est et l’Ouest, et cherche finalement des moyens de s’adapter aux differentes cultures. Mots-Cles: culture, difference, Est, Ouest
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".