How Does China Introduce Returned Overseas Students and Scholars to Start Their Own Business? Experiences, Challenges and Suggestions
Bibliographic record
Abstract
Stating from the “Recruitment Program of Global Experts” announced by the central personnel work coordination group in December 2008, the talents introduction program of China has been carried out step by step across the country. This paper analyzes the challenges that China faces in the world talents competition by reviewing and summarizing the experiences and development history of returned students in starting their own businesses in China and also gives related suggestions at last. Keywords : Returned students; Establish a business in China; China; Experience; Challenges; Suggestions Resume : Les personnels du Groupe de coordination du central a publie en symbole de la («Plannification de mille personnes » ) qui est la « Plannification d’execution du retour des personnels de haut niveau ayant etudies a l’etranger » en Decembre 2008, cette action a ete menee a travers tout le pays. A la conclusion du present texte, nous avons revu le parcours des experiences et le developpement de travail de creation de cariere menes par les etudiants ayant etudies a l’etranger, il a analyse les challenges que les chinois doivent faire face devant la concurrence des talents internationaux, et a egalement formuler des suggestios pertinentes. Mots cles : Etudiants a l’etranger; Retourner dans le pays a la creation d’activite professionnelle, La Chine, L’experience; Defi, Des suggestions
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".