Enforcement of Arbitral Awards in Nigeria and the Jigsaw of Limitation Period: The Need for Compliance with Global Best Practices
Bibliographic record
Abstract
In Nigeria, the limitation period begins to run from the date the dispute leading to the arbitration arose instead of when the award was rendered. While highlighting the rationale and effect of limitation period to the jurisdiction of court, I argue that the period set out in the Arbitration and Conciliation Act (ACA) for enforcement of arbitral awards fails to countenance the inherent delays in Nigeria’s justice system which can be exploited to render the enforcement of an award nugatory. The operationalisation of limitation period unless amended, can be a dissuading factor for choosing Nigeria as a seat of international arbitration which rubs her of the attendant benefits. It is further argued that, anyone, wishing to enforce an award in Nigeria, must ingeniously act timeously to avoid untoward outcome due to the repressive limitation period. This article identifies registration of award pursuant to Foreign Judgment (Reciprocal Enforcement) Act as a leeway to enforce foreign arbitral awards. It compares the practice in Nigeria with jurisdictions like India, Canada, United Kingdom and Ethiopia and draw lessons for Nigeria. It makes a case for amendment of the existing legal framework to bring the law on limitation of time in tandem with global best practices.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".