Bibliographic record
Abstract
Aim: The aim of the study is to provide an insight into the everyday life of the security industry – which has developed differently in many cases, but shares many similarities – by analysing the development of the private security sector in each country.Methodology: Drawing on the academic work of researchers from the states included, the study provides an insight into the private security sectors of Estonia, Mexico, South Africa, Australia, the UK and Canada, as well as the international activities of private military companies, which are scarcely regulated by international law, by presenting sometimes surprising or even instructive examples and practices from abroad.Findings: The author, by examining the functioning of private sectors in different nations, strengthening or hindering regimes and states, demonstrates that while the political debates surrounding the sector are largely similar in content, the responses to them may differ from state to state, and from nation to nation.Value: In addition to the dominant trends in the Anglo-Saxon literature on private security, this study, which presents a new perspective on the development and functioning of the security industry, perhaps even for those who are new to the subject, also interprets concepts such as intersectional governance, anchored pluralism or global North and South, and examines the different theoretical content of private security from one nation to another.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".