2010 Published by Canadian Center of Science and Education 165
Bibliographic record
Abstract
Globalization and economic openness have contributed to increased international negotiations in the 21st century. Despite the enthusiasm for increased global interaction and economic exchange, many people have found that cultural differences have hindered their ability to efficiently conduct business or negotiations due to their lack of understanding of the cultural differences in different countries. This paper explores the impact of religious culture on negotiations. Specifically, we compare and contrast the effects of religious orientation on the negotiating styles of Greater China (Taiwan, Hong Kong and China). The research aims to investigate the role of religious culture as a factor in shaping the negotiation styles of people with different religious beliefs. Casse and Deols ’ model of four negotiation styles was utilized in the research. The research found that there are different negotiation styles among the three countries which vary to significant degrees based upon the religious cultures within Taiwan, Hong Kong and China. These differences have imbued each country with a specific set of values and attitudes relating to their cultures. This study may help people develop more successful negotiation skills by giving them insight into the nuances of negotiations, and by identifying implications for negotiations and areas for future scholarly inquiry.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.839 | 0.588 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".