Le management interculturel comme processus de traduction. Le cas d’une entreprise béninoise
Bibliographic record
Abstract
La littérature scientifique propose plusieurs manières d’analyser l’influence des différences culturelles sur le management des organisations : on peut distinguer à cet égard les positions universaliste, contingente et interprétativiste. À partir de l’analyse d’un cas d’entreprise béninoise qui exporte sa production en Europe, nous examinons dans quelle mesure cette troisième voie permet d’expliquer l’atteinte de performances durables pour les organisations africaines. Nous montrons que c’est moins la combinaison de composantes culturelles occidentales et locales qui garantit l’efficacité, mais plutôt la manière dont les dirigeants parviennent, au moyen d’un travail de traduction, à réduire les dépendances dans lesquelles l’organisation se trouve par rapport à ses différents environnements.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".