Bibliographic record
Abstract
It is an ubiquitous question that it is difficult to get the loans for SMEs not only in the domestic banking sector but also in the world banking sector. One of the important reasons is the difficulty to manage the credit risk. The article analyses and compares the credit risk management of Chinese and foreign banks for small and medium-sized enterprises (SMEs) and gives some suggestions about credit risk management for Chinese commercial banks. Key words: foreign banks, small and medium-sized enterprises, risk management Resume C’est un probleme universel pour les prets bancaires des PMEs dans les banques domestiques et internationales. Une des raisons les plus importantes est la difficultes a manager les risques du credit. Cette these fait une analyse et une comparaison sur les risques du credit par les banques domestiques et internationales aux PMEs, en fournissant des propositions des moyens tres avances des deux derniers pour construire des possible un systeme de management des risques du credit afin de reduire les difficultes des prets des PMEs. Mots-cles : banques etrangeres, PMEs, management des risques 摘 要 無論是在國內銀行業還是在世界銀行業，中小企業貸款難都是一個普遍存在的問題。而導致中小企業貸款難的一個重要原因就是銀行對其風險難以控制。本文通過對中、外銀行對中小企業信貸風險控制的比較，提出了借鑒國外銀行的先進做法，儘快建立合適的風險方法系統的對策建議，以緩解中小企業貸款難的問題。 關鍵詞：外資銀行；中小企業；風險控制
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".