Les déterminants de lâacceptation dâune affectation à lâinternational
Bibliographic record
Abstract
Cette étude porte les raisons de l’acceptation d’une affectation internationale. Alors que l’augmentation du nombre d’entreprises et d’activités internationales crée une pression de plus en plus forte à recourir à la mobilité internationale, ces entreprises éprouvent toujours certaines difficultés à attirer et à sélectionner les bons candidats. Notre étude, effectuée auprès d’individus travaillant dans le domaine de l’ingénierie-construction, visait, dans un premier temps, à connaître quels sont les déterminants individuels, organisationnels et de l’affectation dans l’acceptation d’une affectation à l’international. Dans un second temps, nous avons cherché à vérifier l’importance relative de ces déterminants dans l’acceptation d’une affectation à l’international. Nos analyses ont révélé que deux déterminants individuels et sept déterminants organisationnels ont influence positive significative dans l’acceptation d’une affectation internationale.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".