Crimes Against Humanity: Directing Attacks Against A Civilian Population
Bibliographic record
Abstract
Abstract In international criminal law, to sustain a charge of crimes against humanity, the Prosecution must prove, among other elements, that the perpetrator was involved in an attack directed against a civilian population. In Prosecutor v Fofana and Kondewa, the Special Court for Sierra Leone found that the Prosecution failed to prove, beyond a reasonable doubt, that the civilian population was the 'primary object' of the attack and acquitted the accused on the counts of murder and other inhumane acts as crimes against humanity. The Appeals Chamber accepted this view. However, it reversed Trial Chamber I on the ground that the Prosecution evidence did establish that the civilian population had been the primary, as opposed to incidental, target of the attack. The author suggests that this is an error resulting from the undue jurisprudential pre-occupation with the meaning of 'primary' in relation to the notion of attack against a civilian population. Instead, the inquiry should focus on whether the civilian population was 'intentionally' targeted in the attack, notwithstanding that it may not have been the primary object of the attack. He submits that this approach would be consistent with the classic theory of mens rea in criminal law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".