The Special Tribunal for Lebanon Swiftly Adopts Its Rules of Procedure and Evidence
Bibliographic record
Abstract
The Judges of the Special Tribunal for Lebanon (STL) recently adopted the Rules of Procedure and Evidence to guide the work of the court in bringing to justice those responsible for the attack of 14 February 2005 that resulted in the death of then-Lebanese Prime Minister Rafiq Hariri (‘the Hariri Attack’) as well as related attacks. These provisions draw heavily on analogous instruments of the International Criminal Court and the International Criminal Tribunals for the former Yugoslavia and Rwanda. However, they also contain a number of innovations, including the enhanced role of the Pre-Trial Judge, the establishment of an independent and empowered Defence Office and the possibility of trials in absentia. The review carried out in this article is not a comprehensive analysis of every provision of the Rules of Procedure and Evidence. Instead, the purpose is to describe the key features of this instrument and, in doing so, to highlight points of interest, intersection and divergence in comparison with the analogous instruments of other International Criminal Tribunals. As such, the exegesis is intended to provide an overview of the procedural framework of the STL that will be of use to scholars and practitioners alike.
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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 | 0.046 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".