Report of the UN Working Group on mercenaries, 'Trends and challenges in the financing of mercenaries and related actors' A/79/305
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 selfdetermination examines the trends and increasing challenges presented by the financing of mercenaries and mercenary-related actors. While financial elements are included in the various international and regional legal definitions of mercenarism and the criminalization of the financing of mercenarism, the actual financing of mercenaries and related actors is largely underexamined. In the present report, and for the first time, the Working Group scrutinizes the methods and routes used by multiple primary and secondary actors to fund mercenarism around the world, at both the macro and micro levels, including traditional and alternative banking systems. It further explores the links between mercenarism and the exploitation of natural resources, and the connections to transnational organized crime and other illicit activities. In shining a spotlight on the financing of mercenarism, the Working Group presents an overview of the financial environment in which mercenarism thrives, and highlights the important connections between the involvement of mercenaries and related actors in armed conflicts, the resulting prolongation of armed conflicts and the consequent violations of human rights and international humanitarian law. The Working Group concludes that better regulation of the routes used to finance mercenarism is crucial.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".