Bibliographic record
Abstract
This paper focuses on the issue of wrongful convictions as it emerged in the US during the nineties and subsequently gained attention throughout Europe. The first paragraph focuses on the factors that have brought the issue of wrongful convictions to light and on the impact that the US experience has had on the European Criminal Systems’ acknowledgment of the problem. The second paragraph suggests that the perspective of the Italian jurist might be privileged when confronted with the topic of wrongful convictions, as the Italian criminal justice system was designed to combine the best aspects of both inquisitorial and adversarial systems. For this reason, one would expect the Italian system as generating few wrongful convictions. Facts and figures, however, do not support this expectation. The third paragraph therefore focuses on those that might be the main causes for wrongful convictions within the Italian system, and it subsequently points out one major flaw of the Italian approach to the issue of wrongful convictions: the absence of a national database providing detailed information on previous cases of wrongful convictions. The paper then takes the US National Registry of Exonerations and the establishment of CIUs as positive examples from which Italy should learn. The conclusive paragraph highlights one positive aspect of the Italian system, i.e. the limitations to plea bargaining, and suggests that they might be taken as an example in other countries’ reforms of such mechanism.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".