Establishing Efficient Social Credit System in China from American Experience of Social Credit System
Bibliographic record
Abstract
This paper analyses American social credit system. Social credit is defined as the belief that the provisioning capabilities of a nation are a result of collective effort and should be used in a democracy way as a fund from which each citizen receives an equal share in the form of a basic income. 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, intermediary agency, credit consciousness, credit organizations. Resume: Le present article analyse le systeme de credit social americain. Le credit social est defini comme la croyance que la capacite d’approvisionnement d’une nation est le resultat de l’effort collectif et devrait etre utilisee de facon democratique comme un fonds dans lequel chaque citoyen peut recevoir une part egale en forme d’un revenu de base. On devrait confronter beaucoup de difficultes sans un systeme efficace de credit social. Ainsi, un systeme de credit social a l’echelle nationale doit etre etabli le plus vite possible pour promouvoir le developpement du credit social. Le present article explore ce qu’il faut faire en faveur du developpement du systeme de credit social. Mots-Cles: investigation de credit, systeme de credit social, agence intermediaire, conscience de credit, organisations de credit
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".