Private security regimes: Conceptualizing the forces that shape the private delivery of security
Bibliographic record
Abstract
There is as much diversity within the private security industry as there are differences between public and private security providers. Whereas comparisons of the two modes of delivery have kept criminologists and economists fairly busy over the years, internal variations have not attracted the same level of interest. In the current environment, binary classifications such as the public/private security dichotomy might be too generic to capture the broad spectrum of unique security arrangements being adopted by various organizations. The aim of this article is therefore to offer an alternative conceptual framework that can account for the broad range of mechanisms responsible for the diversity of private security arrangements observed in late modern societies. The term ‘security regime’ defines the convergence of internal forces and environmental constraints that determine the conditions under which security is produced and exchanged by an organization. The four key dimensions (focus, risks, utility and constraints) that characterize a specific security regime were identified from interviews conducted with more than 50 security managers. The security regime approach should expand our knowledge of the various causes that facilitate, empower or hinder public–private relationships.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".