The Judicial Control Enforced over the Formation of the Arbitration Body in Pursuant to the Jordanian Arbitration Law
Bibliographic record
Abstract
Nowadays, arbitration has been receiving much attention. Such attention can be manifested through enacting national legislations and international agreements to regulate it. Such legislations and agreements address the way of choosing arbitrators and the conditions of obtaining membership in the arbitration body. The judicial control is enforced on the arbitration process, because the judiciary is considered the one that has jurisdiction over the settlement of disputes. Such control is enforced to ensure that the arbitral awards are unbiased and impartial. It’s enforced to reach a sound arbitral award that is free from faults. It’s enforced to ensure that nothing shall affect the formation of the arbitration body and its arbitral award. The present study aimed to explore the extent of control enforced by judiciary on the appointment and dismissal of arbitrators and the consideration of the assignment of arbitrators as void. It aimed to identify the extent and limits of this control. Thus, it aimed to identify the way in which the Jordanian legislator regulated these matters. The researchers of the present study adopted an analytical approach to analyze the legislative texts listed in the Jordanian arbitration act and the comparative acts. They also analyzed the relevant judgments issued by the Jordanian court of cassation.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".