Bibliographic record
Abstract
‘When you think of private security and international politics, what is the first image that springs to mind?’ Over the past few years, we have asked this question dozens of times to groups and audiences in numerous countries and contexts. The answers have been remarkably uniform, usually revolving around burly men in combat fatigues, wrap-around sunglasses and automatic weapons. This is no great surprise: the return of mercenary activities in Angola and Sierra Leone in the immediate aftermath of the Cold War and the extensive involvement of private contractors in both Iraq and Afghanistan have justifiably placed corporate soldiers and private military companies (PMCs) at the centre of much public debate and scholarly enquiry. Yet the growth and impact of private security extends far beyond the spectacular activities of corporate soldiers and the increased involvement of private companies in warfare and military affairs. In almost every society across the globe, private security has become a pervasive part of everyday life, and in many countries private security personnel now outnumber their public counterparts by a considerable margin. Recent decades have also seen the emergence of private security companies (PSCs) that operate on a global scale. The world’s largest PSC, Group4Securicor (G4S), is present in over 110 countries, and, with 585,000 employees, it is the biggest employer on the London Stock Exchange. Engaged in the seemingly mundane protection of life and assets – the guarding of workplaces, shopping malls and universities, the monitoring of alarms and closed-circuit televisions (CCTVs), the provision of risk assessment and management – this aspect of security privatization has become so integrated into our daily activities of work and leisure as to go mostly unnoticed. Perhaps for this reason, it is also the untold story of security privatization in international politics.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.226 | 0.090 |
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".