The Study on Credit Management Pattern in Young High-tech Enterprises in China
Bibliographic record
Abstract
With the progress of China’s market economy, small and medium-sized high-tech enterprises have become a strong driver for China’s robust economic growth. However, difficulty in financing has become a major obstacle restricting the development of these enterprises. A fundamental solution to this problem is to improve the management of small and medium-sized high-tech enterprises’ credit. The present paper attempts to design a credit investigation system suited to China’s realities with reference to international theories and practices in this area. Key words: small and medium-sized high-tech enterprises, the issue of credit, credit investigation Resume Avec le developpement de l’economie de marche de la Chine, les petites et moyenne entreprises de hautes et nouvelles technologies sont devenues les forces vives de la croissance economique chinoise. Alors la difficulte de la reunion des fonds jettent obstable au developpement de ces entreprises. La solution de ce probleme consiste a perfectionner le systeme de la gestion de credit dans ces entreprises. Sur la base des experiences avancees internationales sur les concepts et les methodes de la gestion de credit, cet essai tente de concevoir un systeme de la gestion de credit a la chinoise. Mots-cles: petites et moyenne entreprises de hautes et nouvelles technologies, probleme de credit, gestion de credit 摘要 隨著中國市場經濟步伐的深入，中小高新技術企業現已成為中國經濟增長中最富有活力的一支生力軍。然而融資困難卻成為制約這些企業的發展的重要障礙。要解決中小高新技術企業融資難的問題歸根結底是要完善中小高新技術企業信用管理體系。本文在學習、借鑒國際先進的征信理念和方法的基礎上，設計建立起適合中國的征信管理系統。 關鍵詞：中小高新技術企業；信用問題；征信管理
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".