Wrongful convictions in Spain: Systematic analysis of judgments from 1996 to 2022
Bibliographic record
Abstract
A comprehensive analysis of wrongful convictions in Spain was conducted. Out of 447 Supreme Court judgments made between 1996 and 2022, 243 cases involving a successful appeal made by a person claiming their innocence were examined in terms of the characteristics of wrongfully convicted individuals, the crime types, and the factors contributing to these judicial errors. An average rate of nine wrongful convictions per year was found, mostly for crimes against public safety and property, with a significant overrepresentation of foreign citizens. Legal professionals' misconduct was identified as the main factor contributing to these wrongful convictions. The mean time between the judgment and the conviction being overturned was around 4.5 years. More than half of the cases were reopened due to evidence indicating that the alleged crime never occurred. While new evidence was the primary reason for reopening cases, only 3 % were reopened based on DNA evidence. The systematic methodology used in this research may serve as a model for future studies on wrongful convictions in other countries. To reduce wrongful convictions in Spain, several key measures must be implemented. Legal representation should be mandatory for all individuals accused of crimes, without exception. Legal professionals must receive enhanced training to minimize judicial errors. Furthermore, stricter forensic protocols should be established, and forensic experts must be properly accredited to prevent the misapplication of scientific evidence in legal proceedings. Additionally, reforms are needed to ensure that plea bargains are subject to more rigorous scrutiny, and that minor crimes are properly investigated.
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 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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| 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".