The Exclusionary Rule in the Criminal Law of Muslim Countries and Common Law
Bibliographic record
Abstract
Proof of criminal cases is based on the evidence obtained in the criminal process. In this regard, the rules of procedure may sometimes be violated in this process, in which case the legal validity of the obtained evidence has become one of the most important challenges of litigation proof systems. In the present article, through a descriptive-analytic method, this issue has been studied in the two countries under the legal system of Common Law, England and Canada, in comparison with the approach of Islamic countries. The study demonstrates that in some countries, such as Turkey, a strict theory of formal exclusion has been adopted, which leads to the absolute invalidity of such evidences. Conversely, in other systems, such evidences are valid, unless, in accordance with the substantial exclusion theory, as in Iraq, the judge finds that substantive laws have been violated, or, like Iran, consistent with the textual exclusion theory, the statute stipulates the invalidity of evidence. Meanwhile, some countries, such as Algeria, in a position analogous to the United Kingdom and Canada, have taken combined positions from the two perspectives of substantial and textual exclusion. It seems that according to the criteria of Islamic jurisprudence, taking into account certain standards, such evidences can be effectless in the judicial system based on authorized judges who are not fully qualified.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".