Bibliographic record
Abstract
Abstract This article argues that Liberia owes a duty under both international humanitarian and human rights law to investigate and prosecute the heinous crimes, including torture, rape and extra-judicial killings of innocent civilians, committed in that country by the warring parties in the course of fourteen years of brutal conflict. Assuming that Liberia owes a duty to punish the grave crimes committed on its territory, the article then evaluates the options for prosecution, starting with the possible use of Liberian courts. The authors argue that Liberian courts are unable, even if willing, to render credible justice that protects the due process rights of the accused given the collapse of legal institutions and the paucity of financial, human and material resources in post-conflict Liberia. The authors then examine the possibility of using international accountability mechanisms, including the International Criminal Court, an ad hoc international criminal tribunal as well as a hybrid court for Liberia. For various legal and political reasons, the authors conclude that all of these options are not viable. As an alternative, they suggest that because the Special Court for Sierra Leone has already started the accountability process for Liberia with the indictment of Charles Taylor in 2003, and given the close links between the Liberian and Sierra Leonean conflicts, the Special Court would be a more appropriate forum for international prosecutions of those who perpetrated gross humanitarian and human rights law violations in Liberia.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.026 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 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".