Why Prosecuting Aggression in Ukraine as a Crime Against Humanity Might Make Sense
Bibliographic record
Abstract
Abstract The idea that aggression can and maybe should be prosecuted in some instances as a crime against humanity is a marginal one that has nonetheless been floated for a while. This article revisits the idea in the context of efforts to prosecute the leaders of the Russian aggression in Ukraine. It argues that the case that aggression is a crime against humanity has been framed along excessively reductionist lines focusing on ‘other inhumane acts’ as a predicate offence. Instead, the article suggests that there can be a deep overlap between the notion of an armed attack against a state as defining aggression, and the notion of a ‘widespread or systematic attack against a civilian population’ as the chapeau of crimes against humanity. Working at this intersection, it is suggested, makes sense of the special place of aggression as an offence generative of many others, as well as the particular sovereign deliberateness involved in launching an attack. The article explores some of the concerns that such a prosecution might trigger, including that it misses the opportunity to prosecute aggression as such, is in bad faith, or does not cover significant portions of what is rightly considered wrong about aggression. The article concludes in favor of an imaginative take on the substantive law resources that are there rather than the search for new jurisdictional solutions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".