Bibliographic record
Abstract
Victim participation is one of the most innovative aspects introduced in the legal framework of the International Criminal Court (hereinafter – ICC), which has not featured in the practices of other international criminal courts and tribunals. The approach of the ad hoc tribunals to victims was very ‘consumer like’ because victims were solely used as witnesses to testify about the crimes attributed to the accused, but they were not granted broad participatory rights in the proceedings. The drafters of the Rome Statute acknowledged wide-ranging interests of victims who, apart from seeking reparations, have a genuine interest in having their voices heard by expressing their views and opinions throughout legal proceedings, and validating their experiences by being part of the truth seeking process in the court of law. This commentary scrutinizes a number of early decisions in the Lubanga case that have firmly established practices in dealing with victims and were largely followed by other chambers. Although the jurisprudence has received a mixed bag of reviews in professional and academic circles, it is important to highlight the contribution of the judges to the interpretation of the victim participation framework that can be fine-tuned in forthcoming proceedings before the Court.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.037 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".