Overview on Online Arbitration and Procedures (Jordan as an Example)
Bibliographic record
Abstract
It is noteworthy to indicate the online arbitration is the natural evolution of the arbitration rules in their traditional form; since the increase in commercial and civil transactions and the slowness of litigation procedures along with the desire to expedite them in the dispute parties and to save effort and expenses led to the increased demand on arbitration in general and on online arbitration in particular, to face the requirements of transaction evolution from the traditional form into the online form, the matter that led transactors in ecommerce to thinking about and working on finding mechanisms alternative to the traditional in dispute settlement through the use of mechanisms based on the same technology used in entering into electronic transactions, rending the settlement electronic as well, mainly based on the world communication network with no need for the dispute parties to be available at a single place, i.e. the development introduced in the commerce sector using electronic means has not been confined to the activation of this commerce and facilitate the dealings thereof, but it also has extended to the use of these civil vehicles in settling the disputes arising there form. Key words: Arbitration; Online arbitration; Ecommerce; Uncitral; Dispute settlement
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".