Bibliographic record
Abstract
The situation in comparative law with regard to the judicial review of arbitral awards need a harmonization of different legal systems and uniform judicial interpretation in practice with regard to the scope of judicial review of arbitral awards.Expansion of judicial review so that the merits of the case, even to the extent relating to a point of law, could be revisited by national courts seems to be undesirable.Judicial review of the merits of arbitral awards by national courts clearly runs the risk of impinging upon arbitration as an effective method of dispute resolution.Parties to an arbitration agreement can no longer be confident that an arbitral award, once rendered, is final.Thus, an appropriate model of judicial review seems to lie in the recognition of the necessity of judicial review, but basically limiting it in scope to procedural irregularities and violation of due process.It follows that the only recourse permitted is setting aside, and this recourse can only be relied upon in accordance with the norms of the applicable law on arbitration, which usualy sets a list of grounds for setting aside and defines the time limit within which a motion to set aside may be submitted.Rights of appeal and review in a jurisdiction can seriously frustrate the advantages of international arbitration.
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 | 0.064 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| 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".