Ascertaining the Effect of Limitation of Time for Recourse against International Arbitral Award in Nigeria
Bibliographic record
Abstract
Abstract Unlike article 34 of the UNCITRAL Model Law which prescribes three months limitation period for recourse against arbitral award, section 48 of the Nigerian Arbitration Act (ACA) does not specifically contain any limitation period. However, section 29(1) of the ACA which has general application provides for three months limitation period from the date of award within which a party can apply to set aside an award on the ground that the arbitral tribunal exceeded its scope of submission. The aim of this paper is to ascertain the scope, application and effect of the relevant provisions of the ACA by evaluating relevant judicial decisions. This paper adopts the doctrinal methodology by relying on relevant statutes, judicial decisions and literature. The paper points out the issues arising from computation of time as to whether limitation time starts to run from the date of award or when the award is received by the parties or their legal representatives and calls for judicial rethinking. The paper finds that the virtual repetition of the grounds for recourse against international arbitral award in the grounds for refusal of recognition and enforcement may give an unsuccessful party a bite of another cherry. In order not to defeat the essence of the limitation period, the paper recommends necessary legislative reforms.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".