Les amnisties et le droit international : recherche sur l'état du droit
Bibliographic record
Abstract
The use of amnesty measures at the end of a conflict is not a new phenomenon. This practice can be found in peace agreements from mid-17th century. However, the relatively recent rise in international and regional jurisdictions, both criminal and dedicated to the protection of human rights, have led to a reconsideration of this practice, especially as concerns international crimes. In the context of this reconsideration, numerous jurists and international organizations affirmed that there exists an absolute prohibition of amnesties for international crimes. This article contends that while it is generally prohibited to offer amnesty for international crimes, this prohibition is not of an imperative nature. It would thus be possible, depending on each situation, to offer such amnesties. This is done in two steps. Firstly, we search within international recognitions of states’ power to grant amnesties, in order to observe the existence of such an inherent prohibition. Secondly, we explore the different limitations to states’ power to grant amnesties stemming from their various international obligations. It results that while there exist numerous constraints that circumscribe the possibility for states to implement amnesty measures for international crimes, they remain possible depending on necessity or the impossibility of enforcement of criminal action.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".