Limitations of the Ordinary-Crimes Approach to the International Crime of Rape: the Case of Uganda
Bibliographic record
Abstract
Abstract Not many states have effective national laws on prosecution of international crimes. Presently, of the 124 states parties to the Rome Statute of the International Criminal Court (Rome Statute), less than half have specific national legislation incorporating international crimes. Some faith has been placed in the ordinary-crimes approach; the assumption being that states without effective laws on international crimes can prosecute on the basis of ordinary crimes. This article assesses the practicality of this approach with regard to the crime of rape in Uganda. Based on this assessment, the author draws a number of conclusions. First, that there are glaring gaps in the Ugandan definition of rape, making it impossible for it to be relied on. Secondly, although national courts have the option to interpret national laws with a view to aligning them with international law, the gaps salient in the definition of ordinary rape are too glaring; they cannot be remedied by way of interpretation without undermining the principle of legality. Thirdly, prosecuting the international crime of rape as an ordinary crime suggests that approaches applicable to the prosecution of ordinary rape will be invoked. Because these approaches were never intended to capture the reality of the international crime of rape, the ordinary-crimes approach remains illusory.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| 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".