Cross-Cultural Interaction: What We Know and What We Need to Know
Bibliographic record
Abstract
Pervasive forms of worldwide communication now connect us instantly and constantly, and yet we all too often fail to understand each other. Rather than benefiting from our globally interconnected reality, the world continues to fall back on divisiveness, a widening schism exacerbated by some of the most pronounced divisions in history along lines of wealth, culture, religion, ideology, class, gender, and race. Cross-cultural dynamics are rife within multinational organizations and among people who regularly work with people from other cultures. This article reviews what we know from our scholarship on cross-cultural interaction among expatriates, negotiators, and teams that work in international contexts. Perhaps more important, this article outlines what we need to learn—and to unlearn—to be able to see diversity as an asset in helping individuals, organizations, and society to succeed rather than continuing to understand it primarily as a source of problems.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.021 | 0.065 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".