Promoting compliance of private security and military companies with international humanitarian law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it