Wrongful Convictions with Prison Sentences in Spain
Bibliographic record
Abstract
Researchers worldwide have extensively explored the factors contributing to wrongful convictions and the characteristics of individuals affected by these miscarriages of justice for over a century. Despite these global efforts, limited research has been conducted on this issue in Spain. This study seeks to address this gap. We trained coders to assess available review judgments issued by the Spanish Supreme Court from 1996 to 2022. We identified 89 cases of individuals wrongly sentenced to deprivation of liberty. Our findings indicated that 92% of those wrongfully convicted were male, with the majority having a prior criminal record. Most exonerations involved minor crimes, and 85% of individuals were sentenced to less than 4.5 years of deprivation of liberty. Professional misconduct emerged as the primary contributing factor, followed by the misapplication of forensic science, misidentifications, false testimonies, and false confessions. This project sheds light on wrongful convictions in Spain, emphasizing the need for comprehensive measures to address this issue. The current results have practical implications for justice professionals, policymakers, and legal practitioners. It is crucial to educate professionals in the judicial system on the causes of judicial errors, the biases that may influence them, and best practices to improve processes and reduce the occurrence of wrongful convictions.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".