Bibliographic record
Abstract
IntroductionComplicity is an essential mode of liability for core international crimes.It is particularly well-suited to attach liability to those who do not physically perpetrate the crime.In the context of international criminal justice such persons include senior members of military and political leadership.The statutes of the ad hoc tribunals, hybrid courts and the International Criminal Court (ICC) expressly provide for different forms of complicity.Domestic legal systems recognize it in one form or another.Complicity in the statutes of the ad hoc tribunals and hybrid courts includes planning, instigating, ordering, 1 aiding and abetting.2 The ICC Statute encompasses a slightly different list of complicity variations: ordering, soliciting, inducing, aiding and abetting, and contributing to the commission of a crime by a group of persons acting with a common purpose.3 1 'Ordering' as a form of complicity should be distinguished from 'superior responsibility'.The former, unlike the latter, does not require superior-subordinate relationship between the order giver and the perpetrator so long as it is demonstrated that there existed the authority to order.Responsibility of the order giver derives from the wrongful act of the principal rather than the formal link between the two participants in the crime.2
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".