Position of Commercial Arbitration in Resolving Disputes among Customers and Banks in Iran
Bibliographic record
Abstract
In today’s business environment and financial markets, banks are responsible for financial intermediaries and their relationships with customers are established in form of signed contracts. We are witnessing disputes in monetary transactions; thus, parties tend to resolve their conflicts outside the framework of court due to continue cooperation in the future and preserve the value of money and the principle of confidentiality. This research has been conducted to determine the position of commercial arbitration in resolving disputes among banks and customers. Research method is descriptive-analytical and its practical aspects can be used in the banking system. Data has been gathered from theoretical library discussions, the ideas of legal experts, the principles finance and banking sciences, and banking conventions. The results indicate that banks do not like to refer files to arbitration and monetary market has no arbitration committee to resolve disputes. Therefore, banks have used alternative methods such as negotiation and referral to banking expert; in some cases, the role of expert is close to arbitrator. In other cases, resolving the dispute does not arbitration with respect to social order and legislation. Based on findings, main banking services are provided in the form of a contract written by banks in the framework of the Article 10 of the civil law. This contract contains terms. Customers have to accept the terms and sign the contract; otherwise, banks will not provide the considered services.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".