‘One of the Challenges that Can Plausibly Be Raised Against Them’? On the Role of Truth in Debates about the Legitimacy of International Criminal Tribunals
Bibliographic record
Abstract
International criminal tribunals (ICTs) are epistemic engines in the sense that they find (or claim to find) factual truths about such past events that qualify as genocide, crimes against humanity and war crimes. The value of this kind of knowledge would seem to be beyond dispute. Yet, in general the truth-finding aspect of ICTs plays only a very limited role in the often heated debates about their legitimacy. Furthermore, those who actually do address the issue seem widely divided as to whether critiques of the epistemic function of ICTs in fact constitute, in Andreas Føllesdal’s words, one of ‘the challenges that can plausibly be raised against them’ – and if so, in what ways. In this paper, I address the first of these questions asking whether truth-finding should at all be considered a desideratum for ICTs. To this end, I discuss the widespread claim that it should not because the legal truth found in ICT judgements is in fact sui generis; i.e. something categorically different from ordinary truth because exclusively tied to and determined by the legal process as defined in accord-ance with ideals of due process/fair trial. I argue that this position is ill-founded. Properly under-stood, truth in law is intimately connected to ordinary truth. Truth-finding capacity therefore does belong in legitimacy debates as a challenge that can plausibly be raised against them. This, in turn makes it relevant, in future research, to map, analyse and interrelate the various critiques that have been launched against the actual truth conduciveness of ICTs.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.021 | 0.053 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".