The Game Analyses of the Effect of Bank Claim, Penalty and Compensation to High Educational Aid-Loan
Bibliographic record
Abstract
The paper mainly researches behavior of banks and students which affects the efficiency of Chinese high-educational aid-loan. Using the game theory, the paper analyzes the behavioral selection of bank and students in the domestic process of education aid-loan. The paper emphatically anatomizes the impact of the reliability of the bank’s claim, the intensity of penalty and the degree of the compensation to the behavior of banks and students. Gets the conclusion that, under the condition of the credit system lagging, the government should intervene to reduce the cost of the claim, raise the success probability of the claim and increase the degree of the penalty to the students who default in loan contract，to ensure the healthy development of the education aid-loan. Key words: Behavior Analysis, Educational Aid-Loan, Game Theory Resume: Cet article examine principalement les comportements des banques et des etudiants qui influent l’efficacite du pret d’etudes superieur chinois. Utilisant la theorie du jeu, l’article analyse la selection comportementale des banques et des etudiants dans le processus du pret d’etudes. Il disseque categoriquement les impacts de la fiabilite de la reclamation de la banque, l’intensite de la penalite et le degre de la compensation sur les comportements des banques et des etudiants. Il en resulte que, dans le contexte du systeme de credit en retard, le gouvernement doit intervenir pour reduire le cout de la reclamation, augmenter la probabilite de succes de la reclamation et elever le degre de la penalite infligee aux etudiants qui ne s’aquitte pas de leur pret, et enfin pour assurer le developpement sain du pret d’etudes. Mots-Cles: analyse comportementale, pret d’etudes, theorie du jeu
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".