Promoting compliance of private security and military companies with international humanitarian law
Bibliographic record
Abstract
Abstract Private security and military companies have become a ubiquitous part of modern armed conflict and post-conflict reconstruction. Their diverse clients include governments in the developed and developing world alike, non-state belligerents, international corporations, non-governmental organizations, the United Nations, and private individuals. The implications of this proliferation of private security and military companies for international humanitarian law and human rights are only beginning to be appreciated, as potential violations and misconduct by their employees have come to light in Iraq and Afghanistan. The author critically examines the theoretical risks posed by private military and security company activity with respect to violations of international humanitarian law and human rights, together with the incentives that these companies have to comply with those norms. Empirical evidence is also presented to expand on this theoretical framework. Taking a multidisciplinary approach, the author draws on law, international relations theory, criminology, economics, corporate strategy and political economy, as well as psychology and sociology, to analyse the competing “risk-factors” and “compliance levers” that interact at each level of private military and security company activity to enhance or reduce the likelihood of a violation occurring. These findings are then applied by the author to assess emergent measures to deal with private security and military companies outside the legal sphere, including a programme of the International Committee of the Red Cross and the advent of the International Peace Operations Association.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".