Bibliographic record
Abstract
Not many scholarly writings deal with sentencing practices of the ICTY and the ICTR. When compared to frequently discussed topics within the subject matter jurisdiction of the ad hoc Tribunals, they are relatively scarce. Both of these Tribunals’ Statutes provide a sentencing regime encompassing, inter alia, a reference to national sentencing practice. It is the author’s view that the provisions of the statutes and rules of procedure and evidence may violate the nullum crimen nulla poena sine lege principle in certain respects. Without a doubt, the rationale underlying the Second World War precedents with respect to sentencing does not accurately reflect the status of international law norms today. As such, the evolvement of human rights law must be reflected in the current sentencing practice of the ad hoc Tribunals. Moreover, the ICTY and ICTR interpretation of the international sentencing mechanism provides inconsistent justice, which in turn harms the legacy of these ad hoc Tribunals. In fact, an accused could successfully argue that life imprisonment as provided by rules 101 of the Rules of Procedure and Evidence of both ICTY and ICTR are not prescribed by law. Accordingly, the ICTY and ICTR provisions regarding sentencing should be amended.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".