Ghana's Legal Framework for the Constitutional and Statutory Application of Arbitration
Bibliographic record
Abstract
The legal framework in Ghana comprises a range of legislations that establish the fundamental principle of addressing specific concerns through arbitration. The compulsory nature of this arbitration system significantly diverges from the consent-based arbitration framework. This Article provides an analysis of the legislative framework that governs statutory arbitration in Ghana and argues that it is crucial to thoroughly comprehend statutory arbitration as a mechanism within administrative law. Unlike contractual arbitration, which relies on the parties' consent, statutory arbitration receives its authority from legislation. The present study examines the historical origins of statutory arbitration, create an extensive inventory of legislation with provisions for statutory arbitration, and assess the underlying rationales for the adoption of statutory arbitration. The study further examines the issue of consent in statutory arbitration, investigates the procedural aspects of statutory arbitration, and assesses the legitimacy of this specific form of arbitration. The article argues that the notion of statutory arbitration and the provisions found within specific legislations that establish statutory arbitration do not infringe upon constitutional norms. The study concludes that considering the solid legal foundation upon which statutory arbitration is based, the Ghanaian government should contemplate expanding the areas where statutory arbitration can be utilized as a feasible alternative to litigation.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".