THE POLITICS OF TRUTH: ON LEGAL FETICHISM AND THE RHETORIC OF COMPLEMENTARITY
Bibliographic record
Abstract
This paper questions the rhetoric of complementarity between truth commissions and criminal courts in the light of the normalization of a “right to truth” in international legal discourse. Once regarded as mutually exclusive institutions, they are now praised by the international community, human rights and transitional justice advocates as complementary in the fight against impunity. This paper reveals, beyond the consensus on complementarity, how the competition between truth commissions and criminal justice continues as truth advocates strive to negotiate the contours of the “right to truth” in international law. First, it highlights the role of global promoters of the “right to truth” in consolidating the theory of complementarity before international bodies. However, it then stresses their competing visions of complementarity and examines the tensions surrounding the normalization of the “right to truth” in relation to criminal justice and amnesties. Finally, it discusses why these tensions are nonetheless accommodated through the discourse on complementarity, especially in the context of an emerging “truth order” and globalized truth “industry” characterized by the rise of new types of expertise, practices and truth-seeking technologies concerned with the ascertainment and adjudication of mass atrocities.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.114 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".