L'entreprise multinationale à la croisée de la mondialisation et du management interculturel : comment relever le défi posé par la distance linguistique ?
Bibliographic record
Abstract
Résumé Dans cet article, nous proposons un cadre d’analyse pour évaluer la distance linguistique entre le pays d’origine d’une entreprise multinationale (EMN) et son pays d’accueil, ainsi qu’un outil pour la mesurer. Le cadre conceptuel de notre étude s’appuie sur le modèle de classification des entreprises multinationales proposé par Perlmutter (1969) et enrichi par BarTlett et al. (2005). La distance linguistique est mesurée par un indice agrégé, calculé selon la méthode de l’utilité espérée (Von Neumann et Morgenstern, 1947) en présence de critères multiples (Roy, 1985). Les composantes de cet indice sont basées sur les résultats des études antérieures portant sur la distance linguistique. Nous présentons l’outil de mesure et ses composantes, ainsi que les résultats obtenus suite à son utilisation pour mesurer la distance linguistique entre le Canada et quatre pays asiatiques différents.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".