Bibliographic record
Abstract
Spurred by the apparent return of mercenary activities in Africa at the end of the Cold War, and given further impetus and urgency by the substantial involvement of private military contractors in Afghanistan and Iraq, the privatization of security has become one of the most controversial issues in contemporary international politics. Once obscure companies such as Blackwater, Triple Canopy and Erinys have joined with tales of ‘neo-mercenaries’ such as EO in Sierra Leone and Simon Mann’s attempted coup in Equatorial Guinea to become the focus of widespread journalistic coverage, popular books, TV dramas, Hollywood films and increasingly sophisticated scholarly analysis. Yet the privatization of security and its consequences go well beyond the activities of mercenaries and the corporate military. Away from the battlefields, in the day-to-day activities of ordinary life, private security has also become ubiquitous. Less spectacular than the ‘return of the dogs of war’, commercial private security activities, ranging from manned guarding and alarm installation to risk analysis and surveillance, have expanded at a phenomenal rate. Worldwide, the commercial private security market is valued at over $139 billion, and its growth is forecast to continue at an annual rate of 8 per cent to reach a value of $230 billion in 2015. Indeed, what was once described by two prominent criminologists as a ‘quiet revolution’ in security provision has become global in scope.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".