From The Hague to Paris to Montréal: Knowledge transfer and cultural synergy in a multicultural organization
Bibliographic record
Abstract
This article shows how knowledge transfer can be useful in the field of cross-cultural management, in particular to better understand what several authors call cultural synergy. Defined as the ability to take advantage of interactions between people from different cultures to create a dynamic of cooperation and innovation in multicultural organizations, the concept, however, lacks some empirical illustration. Through an unusual field of research, we propose to draw lessons from an artistic organization, Les Grands Ballets Canadiens de Montréal, involved in the remounting of the Kaguyahime ballet by Czech choreographer Jiří Kylián, which will require the collaboration of a Dutch artistic direction, Quebec technicians, dancers from several countries and Japanese and Quebec musicians (Canadian). The focus on knowledge sharing at the center of this encounter allowed us to identify some steps and mechanisms that could be useful both in the theoretical conceptualization of cultural synergy and, from a practical point of view, in its concrete implementation. This will allow us to highlight that, in order to promote, the individuals involved, guided by a management team, will mobilize different identities to create the contact points necessary for cultural synergy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".