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Record W2154696347 · doi:10.1177/1748895808088995

Seeing private security like a state

2008· article· en· W2154696347 on OpenAlexaffabout
Daniel O’Connor, Randy K. Lippert, Dale Spencer, Lisa Smylie

Bibliographic record

VenueCriminology & Criminal Justice · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsCarleton UniversityUniversity of Windsor
Fundersnot available
KeywordsTypologyPrivate securityAgency (philosophy)BusinessState (computer science)Statutory lawCorporate governanceSecurity studiesCritical security studiesPrivate sectorPublic administrationPublic relationsSecurity serviceInformation securityComputer securityPolitical scienceNetwork security policySociologyLawFinanceComputer science

Abstract

fetched live from OpenAlex

Based on a systematic and detailed statutory analysis of 58 jurisdictions in Canada and the United States, this article constructs a modal typology of state regulation of contract private security. State regulation of private security has been neglected despite the fact it has grown across North American jurisdictions in the past two decades. Moving beyond rudimentary regulatory models and focusing on the contract security sector exclusively, five key dimensions of state regulation of private security are identified: governing-at-a-distance, character, identity, training, and information. Whether and how these dimensions relate to management protocols at the security agency level are then examined by combining these results with an analysis of an international survey of contract security managers within these jurisdictions. In turn, each dimension is found to relate to security agency management protocols. Implications for understanding state regulation and future research on private security governance are elaborated.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.027
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.167
GPT teacher head0.386
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2008
Admission routes2
Has abstractyes

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