The use of population-based surveys for prosecutions at the International Criminal Court: A case study of Democratic Republic of Congo
Bibliographic record
Abstract
Combined with traditional qualitative data and testimonies, population-based studies may assist investigators and prosecutors of international judicial institutions in the identification of perpetrator groups and in defining the types of international crimes committed by active perpetrator groups during conflict. This research—based on a secondary analysis of data from a cross-sectional study of the North Kivu and South Kivu provinces and the Ituri district in Democratic Republic of Congo—provides a case study to demonstrate how population-based surveys might have value to International Criminal Court (ICC) prosecutions. These data reveal crimes committed during the conflicts constitute crimes against humanity and war crimes and identify the perpetrator groups most responsible for these crimes. Compared to current prosecutions at the ICC, this research finds that leaders of the Mai-Mai and Interahamwe among other groups not charged by the ICC, were most active in North Kivu and South Kivu provinces for perpetrating sexual and physical violence. Population-based surveys, in addition to traditional qualitative data and testimonies, may aid ICC investigators and prosecutors, particularly in the identification of perpetrator groups and in defining the types of international crimes committed by active perpetrator groups during conflict.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".