Cultural Difference Effects on Business: Holding up Sino-U.S. Business Negotiation as a Model
Bibliographic record
Abstract
Cultural differences affect business negotiation deeply as a part of communication. In a globalizing world today, with international business happened frequently, cultural differences bring influence to communication, any misunderstanding of it may directly affect the business. Therefore, it makes sense for the countries of different cultural backgrounds to understand each other. With the entry into the 21st century and China’s access to the WTO, Sino-U.S. trade and economy has developed rapidly, and it is necessary for the negotiators from two countries to understand the cultural differences and make full use of the beneficial strategies. Key words : Cultural Difference; Sino-U.S. Business; Strategy Resume: Les differences culturelles affectent profondement la negociation d'affaires dans le cadre de la communication. Dans un monde globalise d'aujourd'hui, avec une frequence croissante d’affaires internationales, les differences culturelles exercent une influence a la communication, et toutes sortes de malentendu peuvent affecter les affaires directement. Par consequent, il est logique que les pays d'origine de differentes cultures doivent se comprendre mutuellement. Avec l'entree dans le 21eme siecle et l'acces de la Chine a l'OMC, les echanges economiques sino-americains se sont developpes rapidement, et il est necessaire que les negociateurs des deux pays comprennent les differences culturelles et utilisent pleinement les strategies benefiques. Mots-cles: Difference Culturelle; Affaires Sion-Americaines; Strategie
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".