A Three-dimensional Perspective on Wrongful Convictions in Israel: Organizational-Forensic, Psychosocial and Practical
Bibliographic record
Abstract
It is difficult to find an injustice committed against the citizen by the state that is greater than the conviction of an innocent person. At this stage, it may be tentatively stated that the phenomenon is not insignificant. This theoretical article describes the various aspects of the criminal justice system associated with the undesirable outcome of wrongful convictions. The paper reviews a series of organizational and forensic aspects that could bring about a bias in investigation of the legal truth. Furthermore, a number of psychosocial aspects relating to wrongful convictions, followed by practical aspects are described and discussed. It appears that on the practical level the phenomenon cries out for changes in the law enforcement system (e.g. implementation of the US Innocence Project or the biometric databank) and the need for empirical investigation. It appears that there is still a long way to go before a full understanding can be obtained of wrongful convictions and their prevention. One way or another, the authors are of the opinion that greater academic and public importance should be assigned to the question of wrongful convictions and perhaps turn the issue of truth and falsehood in criminal law into a theoretical and research field in its own right.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".