Bibliographic record
Abstract
Résumé Pour de nombreux expatriés, l’expatriation est une occasion d’acquérir de nombreuses connaissances : des connaissances déclaratives, procédurales, axiomatiques, conditionnelles et relationnelles. Après le retour, nombre d’entre eux feraient face à une baisse de leur niveau d’autonomie, à des difficultés d’adaptation et au manque d’utilisation des connaissances acquises. Dès lors, plusieurs préfèrent quitter leur firme, ce qui provoque une fuite de connaissances considérable. À travers une étude qualitative réalisée auprès de 25 cadres expatriés et de 8 responsables de l’expatriation faisant partie de 15 multinationales françaises, l’article analyse les différentes pratiques en matière de management du retour et propose des solutions afin d’optimiser cette phase de la mobilité internationale. L’optimisation du retour nécessite une conservation du personnel expatrié, puis l’utilisation et le transfert des connaissances acquises à l’étranger.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".