The Admissibility of Evidence Obtained through Human Rights Violations in Ghana: Analysing Cubagee v Asare and Others (NO. J6/04/2017) [2018] GHASC 14 (28 February 2018)
Bibliographic record
Abstract
Abstract The Constitution of Ghana, unlike those of other African countries such as Zimbabwe, Kenya, and South Africa is silent on the issue of the admissibility of evidence obtained through human rights violations. Jurisprudence from Ghana demonstrates that although there had been cases in which the High Court and the Court of Appeal briefly dealt with this type of evidence, the Supreme Court, the highest court in Ghana, had not expressed an opinion on this issue until recently. In February 2018, in the case of Cubagee v Asare and Others , the Supreme Court laid down the criteria that Ghanaian courts have to use in determining the admissibility of evidence obtained through human rights violations. In this article, the author argues that much as this is an important decision, the Supreme Court left some issues unresolved and there is still room for improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".