THE LEGAL BASIS FOR THE AUTHORITY OF PRIVATE POLICE AND AN EXAMINATION OF THEIR RELATIONSHIP WITH THE "PUBLIC " POLICE
Bibliographic record
Abstract
role of modern private security personnel or "private " police, it is important that we ask some more fundamental questions about the legal basis of their authority. On many occasions each day, shoppers, travellers, students, tenants and workers are confronted by these "private " individuals, and requests may be made for searches and questioning. These interactions may occur on private property or in more "public " areas. From time to time individuals refuse to comply, and the question arises: what authority do security agents have? In what circumstances can force accompany non-compliance with a request? In what circumstances can an arrest occur? Accurate legal answers to each of these questions are elusive. The implications for civil liberties are significant. This paper is designed to explore some of these issues. It is clear, from a cursory glance, that the position in 1977, as reported by Canadian researchers, is not much different today: At the very least it is necessary to clarify the powers presently held by private citizens, a matter which, because of the relatively recent upsurge in the size of the private security industry, and the tendency of the legal superstructure to lag behind social reality, has only received very limited consideration, either in the legislature or in the courts (Freedman & Stenning 1977, p. 66).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".