Purpose-based or knowledge-based intention for collective wrongdoing in international criminal law?
Bibliographic record
Abstract
Abstract Due to the distinct nature of international crimes such as genocide and crimes against humanity originating out of and contributing to the pervasive collective character of mass atrocity, the appropriatemens reafor individual commission of these crimes is difficult to pin down. Themens reafor these international crimes has been deliberated, disputed and inconsistently applied, leaving what it means for individuals to intend to commit crimes of mass atrocity mired in confusion. This paper explores the meaning of intentional commission of collective crime, and demonstrates that from both philosophical and legal perspectives, acting intentionally in the context of mass atrocity can be interpreted in different ways, resulting in a condition of international criminal law which is at risk of unpredictability and expressive uncertainty. The paper endorses purpose-based, rather than knowledge-based, intent as the appropriate standard in the context of international crimes by arguing that mere knowledge of outcomes is insufficient.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.056 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".