Impact of International Arbitration Centers on Arab Arbitration Cases: A Comparative Study of the Negative Effects on Arab Dispute Resolution
Bibliographic record
Abstract
This study addresses the impact of international arbitration centers on traditional Arab dispute resolution methods, which are deeply rooted in cultural and religious values. Despite the growing popularity of arbitration centers worldwide, their effects on Arab societies remain inadequately explored. Through a comparative analysis of select Arab arbitration cases, the present study has examined the adverse consequences arising from international arbitration centers. Key factors contributing to these negative effects, including cultural and language barriers, as well as the financial costs associated with arbitration have been investigated. The research objectives encompass understanding the clash between international arbitration and traditional methods and proposing strategies for better integration and coexistence. Drawing on the findings, the present study offers practical recommendations to enhance the collaboration between international arbitration centers and local communities. The study underscored the importance of upholding cultural diversity and advocated for the preservation of community-specific dispute-resolution mechanisms. By shedding light on these complexities, this study has contributed to theoretical advancements and practical solutions for understanding the arbitration’s influence on Arab societies and promoting harmonious coalescence between global arbitration practices and traditional values.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".