Prácticas forenses y violencia en masa: perspectivas contemporáneas y retos investigativos
Bibliographic record
Abstract
We already have forty years of experience in Latin America in the application of anthropology and forensic archaeology to the search for missing persons and investigations of gross violations of human rights and international humanitarian law. Despite this long trajectory of very important work, the most striking protagonists of meta-analysis in the forensic work literature and its impact in recent years have not been the forensic scientists themselves, but social scientists. This article introduces the dossier “Forensic Practices and Mass Violence: Contemporary Perspectives and Research Challenges,” a work that brings together practitioners —including relatives of the missing— with academic researchers, breaking down a structural, social and artificial divide. The joining of forces between academics and practitioners better reflects the work as a whole that includes and highlights the goals concerning search, recovery, analysis, and identification, but also those concerning restitution. This introduction emphasizes debates that are absent in many forensic science journals: the impact of politics on research and the political product of research, although we still have debates about questions of objectivity, neutrality, and the value of a family-driven or family-involved approach. In this dossier, we examine the adaptation and evolution of the discipline from its particular Latin American form, both in the different expressions it has taken in the region and in the way it has been expressed in the work of Latin American professionals in foreign cases such as that of the former Yugoslavia.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".