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Record W2620972567 · doi:10.5539/jpl.v10n3p174

Position of Commercial Arbitration in Resolving Disputes among Customers and Banks in Iran

2017· article· en· W2620972567 on OpenAlexvenueno aff
Saeid Eshragh Abad Shahpori, Zeynab Porkhaghan Shahrezaei

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsArbitrationPosition (finance)BusinessNegotiationOrder (exchange)Financial servicesIntermediaryConfidentialityLegislationOnline dispute resolutionDispute resolutionMediationLawAccountingAlternative dispute resolutionFinancePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.263
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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