Bibliographic record
Abstract
Purpose To remind readers of the decline in America's reputation and the importance of “citizen diplomacy” in addressing the problem. Design/methodology/approach Business for Diplomatic Action (BDA) asked people in more than 100 countries to give advice for Americans who travel outside the US. Their responses formed the foundation for a World Citizens Guide produced and distributed by BDA to US youth who travel and study abroad. Based on the success of this students' guide, a business travelers' guide will be released in the first quarter of 2006. Findings Research confirms that Americans are broadly seen as arrogant, self‐absorbed, ignorant of other cultures and insensitive. These perceptions are at least partially formed by interaction with the Americans who make 60 million trips abroad every year. By following the advice of people in host countries, US citizens who travel can begin to improve America's reputation. Practical implications The article includes 16 specific suggestions that, followed, will make American business travelers better ambassadors for their country. Originality/value Understanding how Americans are perceived is the first step toward modifying arrogant and insensitive behavior. American business travelers who learn to be more sensitive to the foreign cultures they encounter will not only enhance their chances for business success but will improve the perception of their country at the same time.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".