Bibliographic record
Abstract
In this paper, the three-dimension social credit structure made of three vectors including individual credit, organization credit and social credit environment, accordingly, three levels form, they are individual credit and organization credit, social credit environment and organization credit, social credit environment and individual credit. After analyze the interaction between every level vectors. A conclusion is drawn: It is social credit environment that determine the whole social credit condition. This text proceeded with the analysis of the cause of social credit environment of China and put forward the crucial way to improve the credit condition of china. Keyword: Individual credit, organization credit, social credit environment, opportunism Resume: Dans ce texte , la structure tri-dimensionnelle du credit social est compose par trois vecteurs , y compris le credit individuel , le credit d’organisation et l’environnement du credit social , en consequence , trois niveaux de formes y correspondent : credit individuel et credit d’organisation ; l’environnement du credit social et credit d’organisation ; l’environnement du credit social et credit individuel . Apres avoir analyse l’interaction de tous les niveaux du vecteur , il en ressort la conclusion que l’ensemble de la condition du credit social est determine par l’environnement du credit social . Ce texte donne suite a l’analyse de la raison de l’environnement du credit social en Chine et propose des solutions pour ameliorer la condition du credit en Chine . Mots-cles: credit individuel , credit d’organisation , environnement du credit social , opportunisme
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".