Public Corporate Security Officers and the Frontiers of Knowledge and Credentialism
Bibliographic record
Abstract
Introduction In this chapter, we examine public corporate security work. We focus on corporate security in 16 of Canada's municipal governments – referred to throughout the chapter as municipal corporate security (MCS) – and discuss how public corporate security has entered Canada's federal government departments. Conceiving of public corporate security as a new frontier of security provision, we show how it has become part of policing and security networks, as well as how knowledge, technology and strategies from the private security and insurance industries are being transferred into public government. Engaging with sociologies of networked security governance, security consumption and risk management, we argue that public corporate security contributes to the securitisation of spaces through asset protection, risk and liability management, and employee surveillance. We discuss how the work of MCS specifically is animated by a discourse of urban threat, showing how MCS practices in Canadian cities blur the line between policing and securitisation. We also consider the implications of our analysis of public corporate security for understandings of policing, security and public accountability on the frontier of knowledge and credentialism. The latter refers to the increased and at times over-valuing of security educational credentials and the growing demand for them for corporate security work (see also Collins, 1979). New urban security arrangements involving MCS are perhaps best symbolised by an unnamed official routinely watching a pre-screened employee sign for a plastic card allowing access to a newly restricted, non-public urban zone, with pre-screening and zoning determined using assessment tools from the private corporate world. MCS entails uploading a corporate-style arsenal of specialised knowledge, technology and strategy to regulate municipal workers, ‘corporate assets’ and properties. This inevitably also engages disadvantaged people living on the streets or near these properties. MCS securitising aspirations are starting to transform city-owned and leased edifices as well as public spaces like parks, squares and streets used by urban dwellers. MCS departments are implicated in: • surveillance for major public events (which entails threat assessments); • securitisation of municipal buildings and property; • dealing with ‘broken windows’ and other forms of ‘nuisance’, for example liquor consumption as well as homeless people (who in Western Canadian cities especially are disproportionately represented by Indigenous peoples) on municipal property; • legal liability management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".