A CALL FOR A NEW INTERCULTURAL COMPETENCE PLAYBOOK IN MEDIATING SOCIAL TRANSFORMATION
Bibliographic record
Abstract
Every culture is in conflict; conflict is endemic in the process of culture metamorphosis. This paper examines the paradox that conflict is intrinsic to every culture, yet there is little attention to the ‘culture’ norms in resolving that conflict. There are conflicts within cultures and there are conflicts between cultures. The pioneering scholarship on national cultural traits, by original thinkers such as Hofstede and Ting- Toomey in the 1990s, assisted negotiation and dispute resolution and provided a foundation for cultural differentiation. But much has changed in intercultural understanding since then. We are now aware of enormous variations in values, styles, and customs in defined cultures. Today in the 2020’s we are fast becoming a more global interdependent society with high cultural diversity, declining Western power, and rising Asian-led world order in a new era of commercial influence, high-speed digital robotics, and artificial intelligence. We are in an unprecedented global pandemic where cultural norms and expectations are under threat, in individualistic and collectivist cultures. We face existential threats from climate change and environmental catastrophes. We need a new mediator playbook for effective intercultural negotiation and issue resolution.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.021 | 0.056 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.024 | 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".