The Genocidal Forcible Transfer of Children: A Crime Well Established in International Law; Yet Still Not Prosecuted by the ICC
Bibliographic record
Abstract
As the selected ICC case law here previously discussed illustrates, there is a continuing failure to acknowledge and prosecute the recruitment of children (persons under age 18) and/or their use for active participation in hostilities by armed groups or forces (State or non-State) that perpetrate mass atrocities and/or genocide as the ‘genocidal forcible transfer of children’. Rather, such recruitment and/or use when involving under 15s is classified exclusively in terms of war crimes for the first time under international criminal treaty law (the Rome Statute). The latter war crimes are regarded by some as a novel category of international crime though, as previously discussed here, this category of war crimes was already reflected in customary international law. For instance, Schabas states “…the Rome Statute does recognize some new crimes that were probably not covered by the earlier instruments, such as the recruitment of child soldiers …” These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".