Bibliographic record
Abstract
Like it or not, every business—even one conducted from the kitchen table—is global. No matter the industry, employees now routinely travel to other countries or interact with foreign customers, vendors, or fellow employees. Or they conduct business over the phone, via e-mail, or through video links. As a result, they have to understand international customs and etiquette or risk losing customers or botching business relations. And understanding business customs in other cultures isn't merely playing good defense—it often leads to new products or service enhancements that help an enterprise grow. In Passport to Success, Jeanette Martin and Lillian Chaney apply their expertise in business etiquette, training, and intercultural communications to present a practical guide to conducting business successfully around the world. Each chapter in this book presents in-depth information on the business environment and culture in the top twenty trading partners of the United States: Canada, Mexico, Japan, China, United Kingdom, Germany, South Korea, Netherlands, France, Singapore, Taiwan, Belgium, Australia, Brazil, Hong Kong, Switzerland, Malaysia, Italy, India, and Israel. Chapters contain both practical tips and illustrative examples, and the book concludes with a listing of resources (books, magazines, organizations, and Web sites) for additional information. In addition, Passport to Success contains useful overview material that will help business people plan a trip abroad or a campaign to win customers in another country. Besides trade statistics and information on global trade agreements, readers will find information on using the Internet productively to conduct or seek business, how women can succeed in countries with traditional, male-oriented business cultures, how to build cross-cultural relationships, and ways language can enhance—or obstruct—business dealings. Every businessperson is now a player in the global market for goods and services. This book provides valuable tips that will help people avoid missteps and increase their sales and personal success when dealing with counterparts in other countries.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.182 | 0.105 |
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".