The immoral legality of targeting child soldiers : A humanitarian dilemma
Bibliographic record
Abstract
The thesis functions as an overview of the protection of children used in hostilities and their targetability under international law, primarly in international humanitarian law. The author questions the established presumption, that children directly participating in hostilities, shall be equally targetable as their adult colleges and counter parts. In times of conflicts, international humanitarian law offers children a special protection status. The prohibition against recruitment and use of children under the age of 15 illustrates two of the most relevant protective regimes. Despite these protective rules, children directly participating in hostilities remain lawful targets and their special protection status becomes severely less useful. For these reasons, the author argues, that the presumption of equal target ability hinders the Special protection of children to have the practical impact for children used on the battlefield it intened to.The tremendous amounts of child soldiers recruited by non-state armed groups shows that the prohibition against recruitment and use aren't respected. However, the author argues that the ICC juris prudence and national initiatives illustrate that the presumption of equal targetability can be challenged under international law. And, that the special protection for children in times of conflict, and international law as a whole does motivate differanting rules for targeting children versus adults participating in hostilities. For arguments presumably made on moral grounds, military personnel are willing to put themselves at greater risk to spare the life of a child soldiers forced to take part in a conflict, he or she definitely did not start. The Vancuver principles and the The Canadian Armed Forces Joint Doctrine Note shows that states are willing to follow in these footsteps by adding extra precautionary measures in the planning state of a military operation and to completely differentiate the rules for targeting children and adults. Lastly, the author argues that the rules for targeting cultural property ( set out in the 1954 Convention for the Protection of Cultural Property), could be an example on how to differentiate the rules for targeting children. In this regard the author argues that, like cultural property, children directly participating in hostilities should only be lawfully targeable, at last resort, when there is no other way to fulfill the military advantage.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".