Bibliographic record
Abstract
This chapter concludes the study by highlighting the many layers of translationTranslation that take place in the search for the most appropriate modes of liability for mass atrocity. There is a translationTranslation from the collective to the individual; from the notion of ‘every day’ collective criminalityCollective criminality to the circumstances of mass atrocity; from the domestic to the international; and translations of criminal law terminology from languages including German, French and Spanish to English, and between English-language jurisdictions which have different understandings of terminology such as responsibility, liability, and culpability. International criminal lawyers must become more sophisticated comparativists to deal with these multiple layers of translationTranslation . They must also be aware of the policy underlying the domestic models which they depend on, and the policy choice they are making at the international level. A defence of the normatively differentiated system of liability is laid out, emphasising the goals of ICL, the reasons why an objective approach is a better fit for ICL, why a normative theory of culpability should apply, and how this all relates to the deliberative decision-making structures in collective crime. An argument is made the modes of liability are the best way to express these differences, rather than leaving it to sentencingSentencing . Finally it is argued that fair labelling also requires us to distinguish between the intellectual authors of a crime and those on the mere periphery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".