'Never Again': Transitional Justice and Persistent Police Violence in Argentina
Bibliographic record
Abstract
On 31 January 2009, 16-year-old Luciano Arruga disappeared. His case is not an isolated one: more than 213 people were ‘disappeared’ by security forces in Argentina between 1983 and 2012. Certainly, these numbers pale in comparison to what occurred during the last dictatorship, when as many as 30,000 people disappeared. Yet, Argentina has pursued many transitional justice processes to address this past with the goal of nonrepetition or, as the truth commission put it, ‘Never Again’ (Nunca Más). Drawing on interviews as well as media and document analysis, this article analyzes the discursive obstacles faced by actors of social accountability – the media and human rights organizations – when applying lessons from the past to current police violence. I argue that the complex relationship between human rights and security is at the heart of this discursive challenge.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".