Bibliographic record
Abstract
This article examines the causes and prevention of wrongful convictions in Japan. The criminal justice system heavily relies on interrogations, reinforced by systemic flaws. With an exceptionally high conviction rate, judges’ role in determining guilt is largely limited, making prosecutors the key decision-makers. Once indicted, innocent individuals face severe disadvantages. These structural issues contribute to wrongful convictions, particularly through false confessions and accomplice false testimony. Psychological factors such as confirmation bias, recognized globally as a major cause of wrongful convictions, also play a role in Japan. This highlights the universality of wrongful conviction risks and underscores the need for international collaboration. Since transitioning away from interrogation-dependent investigations is difficult, developing an alternative investigative model is essential, further emphasizing the importance of global research. For prevention, Japan has traditionally relied on judges refining fact-finding through evidentiary evaluation and the Cautionary Principle. While this helps ensure logical reasoning, it carries risks, as the principle itself may have flaws and is not always applied consistently. To address the recurring causes of wrongful convictions, a new approach has been proposed in Japan, integrating risk management principles. By systematically analyzing past wrongful conviction cases, this method seeks to prevent future errors. The principle of “learning about and from wrongful convictions” is emerging as a critical theme in global wrongful conviction research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".