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Record W2807950807 · doi:10.1177/0004865818781191

Corporatizing security through champions, condos and credentials

2018· article· en· W2807950807 on OpenAlexafffund
Randy K. Lippert, Kevin Walby

Bibliographic record

VenueAustralian & New Zealand Journal of Criminology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of WinnipegUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCorporatizationCommodificationAccountabilityCorporate governanceMarketizationBusinessPublic relationsSociologyPolitical scienceEconomicsLawEconomyChina

Abstract

fetched live from OpenAlex

This paper argues that corporatization of security is distinct from related phenomena including commodification, privatization, and marketization. Corporatization refers to the spread of the corporate form and therefore to organization and governance, not ownership, and as corporatization expands in the security domain it raises troubling issues due to its secretive and undemocratic features. Corporatization’s conceptual purchase and these issues are shown through exploration of emergent security arrangements in three under-researched, disparate realms with growing global presence: public police sponsorship, private residential urban security, and public corporate security. These revealing realms of security corporatization are established and enhanced by specific techniques and organizational forms, including champions, condominiums, and credentials, respectively. The paper concludes with discussion of implications of corporatization of security for scholarly methods of inquiry and critique as well as for increasing accountability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.359
Teacher spread0.250 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2018
Admission routes2
Has abstractyes

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