Private Military and Security Contractors:Controlling the Corporate Warrior
Bibliographic record
Abstract
In Private Military and Security Contractors: Controlling the Corporate Warrior a multinational team of 16 scholars and a practitioner from political science, sociology, and law address a developing phenomenon: controlling the use of privatized force by states in international politics. Robust analyses of the evolving, multi-layered tapestry of formal and informal mechanisms of control include addressing the microfoundations of the market: the social and role identities of contract employees, their acceptance by military personnel, and potential tensions between them. The extent and willingness of key states—South Africa, the United States, Canada, the United Kingdom, and Israel—to monitor and enforce discipline to structure their contractual relations with PMSCs on land and at sea is examined, as is the ability of the industry to regulate itself. Finally, they assess the nascent international legal regime to reinforce state and industry efforts to encourage effective practices, punish inappropriate behavior, and shape the market to minimize the hazards of loosening states’ oligopolistic control over the means of legitimate organized violence. Together, the volume presents a theoretically-informed synthesis of micro- and macro-levels of analysis, producing new insights into the challenges of controlling the agents of organized violence used by states for scholars and practitioners alike.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".