Wrongful Convictions and Prosecutions in Latin America
Bibliographic record
Abstract
Drawing on a systematic literature review, this paper examines the scholarly discourse surrounding wrongful convictions and prosecutions in Latin America, spanning from 2010 to July 2023, across the WoS, Scopus, and SciELO databases, identifying a set of 50 publications. From a quantitative perspective, the paper inquiries into aspects such as publication year, countries covered, characteristics of the scientific community involved, and the topics addressed. Then, the paper briefly delves into a qualitative analysis of the publications’ content, distinguishing among those that address general aspects, factors contributing to wrongful convictions and prosecutions, correction mechanisms, and compensation mechanisms for wrongful convictions and prosecutions. Although with limitations, the findings provide an overview of research in this area in Latin America, showing that, although scholarship is still scarce compared with other latitudes, the topic has begun to attract interest in recent years in the region.
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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 | 0.026 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".