The mercenary moniker: Condemnations, contradictions and the politics of definition
Bibliographic record
Abstract
Abstract Despite considerable efforts, the concept of the ‘mercenary’ remains ill-defined within the scholarly literature on non-state combatants. In common usage, ‘mercenary’ is intended to function as a descriptive category of combatant, denoting certain unique or transhistorical properties. Instead, however, it is a highly subjective, imprecise and politicized term. This article critically analyses historical, legal and philosophical definitions of ‘mercenary’, and asks whether it is worth retaining the term as an analytical category at all. In short, the answer is no. The article’s exposition of the ‘mercenary moniker’ uncovers the statist political ethic that anchors different interpretations of the mercenary concept. It shows that conceptions of the mercenary are deeply rooted in a Westphalian political ethic of war and conflict that upholds the instrumentality of the state to notions of political community, morality and identity. Accordingly, it argues that ‘mercenary’ should be jettisoned from the academic conceptual vocabulary of non-state combatants, and proposes ‘freelance militant’ as an alternative. Properly contextualized, this alternative could make possible a conceptual vocabulary that is able to clearly distinguish between such freelance militants and other non-state combatants.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".