Bibliographic record
Abstract
This research aims to study evidence rules on the admissibility of illegally obtained evidence, especially in search and seizure. Exclusionary rule requires the courts to exclude all evidence that is seized in the course of unconstitutional and illegal search of person and properties. Its purpose is to deter any illegal investigation by the police and prosecutors. Firstly, this research overviews academic debates and case laws on the exclusion of illegally obtained evidence in the US, UK, Germany, Japan, Canada and Korea. Secondly, the development of exclusionary rule in the US is analysed. This is followed by the review of the Supreme Court ruling in 2007 on the case of illegally obtained evidence in search and seizure in 2006. In Its full bench decision, the Supreme Court reversed its position by deciding that all forms of illegally obtained evidence are, in principle, inadmissible. However, the court may have discretion to allow such evidence that obtained without breach of substantive elements of due process, considering all relevant circumstances. When the exclusion of evidence would bring injustice in criminal justice, the court may also exercise its discretion. Lastly, it analyses the meaning and limits of newly introduced §308-2 of the Korean Criminal Procedure Code. This provision codifies the exclusionary rule, so that evidence, which is not obtained by the due process, is not admissible. To realize this rule in practice, principles and exceptions in applying the rule should be provided more in detail.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".