Recruitment, including predatory recruitment, of mercenaries and mercenary-related actors:Report of the Working Group on the use of mercenaries as a means of violating human rights and impeding the exercise of the right of peoples to self-determination
Bibliographic record
Abstract
In the present report, the Working Group on the use of mercenaries as a means of violating human rights and impeding the exercise of the right of peoples to self-determination examines the recruitment of mercenaries and mercenary-related actors and the phenomenon of predatory recruitment. The recruitment of mercenaries and mercenary-related actors has increased in conflict, post-conflict and conflict-affected contexts, intensifying the risk of violations of human rights and international humanitarian law. The recruitment of mercenaries and mercenary-related actors is conducted by a variety of actors, including States and non-State actors. Examining the mechanisms through which the recruitment of mercenaries takes place, the entities involved in the recruitment, the profile of the individuals recruited, the contexts in which mercenaries and mercenary-related actors are recruited and other relevant aspects surrounding the practice is key to tackling the phenomenon of mercenarism. In this context, the Working Group has observed with concern a trend towards the entrenchment of the phenomenon of predatory recruitment, whereby individuals are recruited in a way that takes advantage of their socioeconomic status and other vulnerabilities and may involve different forms of exploitation. In the report, the Working Group urges States to take an approach that addresses the root causes of recruitment, including predatory recruitment, to tackle the scourge of mercenarism.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".