Bibliographic record
Abstract
When is an expatriate assignment most beneficial to your career? This depends very much on your situation – there is no “one-size-fits-all” answer to this question. This article attempts to describe them. First, it analyses the situation of expatriating, explains the benefits and downsides of expatriating for the young. Illustrate personal factors in expatriate and put forwards the suggestions in the end. Key words: globalization; expatriate; young; suggestionResume: Quand une affectation des expatries peut beneficier le plus a votre carriere? Cela depend,dans une grande mesure, de votre situation - il n'y a pas de reponse de “taille unique pour tous“ a cette question. Cet article tente de les decrire. Premierement, il analyse la situation de l'expatriation et explique les avantages et les inconvenients de l'expatriation pour les jeunes. Ensuite, il illustre les facteurs personnels dans l'expatriation et propose des suggestions a la fin.Mots-Cles: globalisation; expatries; jeunes; suggestion
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".