Commentary on Children, Accountability and Justice: Advancing Restorative Justice for Child Soldiers and Child Pirates
Bibliographic record
Abstract
Jacqueline Salmone’s timely article arguing that the international community should apply a restorative justice approach when dealing child pirates, in the same way their child “soldier” counterparts are treated, sheds a bright light on an area that has largely remained hidden in the shadows. In doing so she presents cogent arguments questioning why the predominate use of retributive justice for dealing with such criminal activity should be privileged over the restorative approach applied to child soldiers that looks to the best interest of the child. As is outlined, the restorative justice approach fosters healing, social reintegration, and serves as a prevention mechanism for re-entry into conflict. It is difficult to argue against these outcomes, and it is not clear why the trial and punishment means applied to adults appears to remain the favoured approach. Notwithstanding the acknowledgement that some children are not faultless and passive victims, it is evident from the article, and especially in respect of its assessment of the “conceptions of childhood”, that the linking of “children” with a purely “retribution” approach seems out of place in the 21st Century. It immediately forces the reader to ask why this has occurred, and what needs to be changed to avoid such a narrow limited view of how child pirates should be treated.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.074 | 0.070 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".