Co-operation or competition? Perceptions and discourses on the role and function of the private security industry in Belgium
Bibliographic record
Abstract
Near the last quarter of the twentieth century, the question of an expanding private security sector in Belgium gained more and more attention among several contemporaries and interested parties. Various commentators, despite showing different professional backgrounds, expressed their worries about the expansion of duties of private security personnel and the alleged shifting of the role and function of the private sector in relation to the public police. This long term changing distribution of security through both state and market providers, deserves further attention. Thus, the main purpose of this draft article is to investigate the different interpretations and perceptions of these commentators concerning the (alleged) blurring of public and private policing functions since the interwar period until 1990. It wishes to critically assess the degree to which the growing security industry was either perceived – in a complementary way – as a partner in crime prevention, or more as a competitor involved in what should be exclusive public policing tasks. Drawing extensively on parliamentary discussions, writings of members of law enforcement bodies, social and legal researchers, and so forth, it provides new findings on twentieth-century perceptions and understandings of a phenomena that seemed, for some, obscure and suspicious.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".