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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".