Advising Defendants about Guilty Pleas before International Courts
Bibliographic record
Abstract
The aim of the article is to convey to the reader the variety of considerations that Defence Counsel before the International Tribunals must take into account when advising accused persons about their pleas. Although there are no formally adopted sentencing guidelines for guilty pleas, certain practices and patterns have emerged in the jurisprudence. The article thus examines the host of mitigating and aggravating factors which the judges have identified in the sentencing judgments following guilty pleas. The plea-bargaining process is also discussed, in particular the deviations between the sentences recommended in the agreements between the Prosecution and Defence, and those handed down by the Trial Chambers—another factor about which accused persons must be advised in deciding upon their pleas. The article concludes by highlighting the challenges faced by Defence Counsel in applying the multiplicity of sentencing features to the facts of each case.
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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".