Bibliographic record
Abstract
Abstract.This paper questions both realist and restorative conceptions of truth commissions, to the extent that both of those conceptions neglect the internal links between truth commissions and criminal trials. Interpreting the requirements of retribution, responsibility and truth-telling, the paper argues that trials and truth commissions should be placed at points on a spectrum rather than in distinct categories, and that the circumstances of political transition explain the divergences in their respective practices. We may see truth commissions and trials as expressing the same aims of justice, though in contextually differentiated ways that modify both the subordinate principles required by the aims of justice and also their institutional expression. Résumé.Cet article remet en question la conception réaliste et la conception réparatrice des commissions de vérité, dans la mesure où toutes les deux négligent les liens internes entre les commissions de vérité et les procès criminels. Interprétant les exigences de rétribution, de responsabilité et de véracité, l'article avance que les procès et les commissions de vérité devraient être situés en divers points du même spectre plutôt que dans des catégories distinctes et que les circonstances de transition politique expliquent les différences entre leurs pratiques respectives. On peut considérer les commissions de vérité et les procès comme traduisant une même “poursuite de la justice”, bien que les différences de contexte modifient dans chaque cas les principes subalternes qu'impose la recherche de la justice et, de ce fait, changent aussi leur expression institutionnelle.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".