Establishing Efficient Social Credit System in China from European Experience of Social Credit System
Bibliographic record
Abstract
This paper analyses European credit system and its experience we can learn from. The paper firstly discusses the three European social credit modes. Then we study the experience in society credit system construction in Europe. Many difficulties would be faced without a sound social credit system, so the establishment of a nationwide social credit system should be sped up to improve the development of social credit. The paper discusses what to do as to the development of social credit system. Key words: credit investigation, social credit system, credit bureau, mode Resume: Le present article analyse le systeme de credit europeen et les experiences dont on peut s’inspirer. L’article presente d’abord les trois modeles de credit social europeens. Puis on apprend les experiences dans la construction du systeme de credit social en Europe. Beaucoup de difficultes devront apparaitre sans un systeme de credit social sain, ainsi l’etablissement d’un systeme de credit social a l’echelle nationale doit-il etre accelere pour promouvoir le developpement du credit social. L’article discute ce qu’on doit faire dans le developpement du systeme de credit social. Mots-Cles: investigation de credit, systeme de credit social, bureau de credit, modele
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".