Policing the corporate image : a case study of in-house security governance and the management of risk in a mass private property in Canada
Bibliographic record
Abstract
'Mass private properties' such as shopping malls, hotel complexes, and large educational, manufacturing and industrial sites increasingly operate as sites of public and social life. Since private interests reign over the policing of these spaces, public life that was once protected and controlled by the state is now policed by private institutions. These changes have resulted in a significant rise in the number of private security personnel employed in Canada, where there are now more than twice as many private security agents as there are public police officers. This development has expanded the ambit of authority held by the 'private police' and those institutions that employ them. This paper is concerned with the nature, scope and extent of 'security governance' in mass private spaces, specifically through the use of in-house, or proprietary, systems of governance. Findings suggest that actuarialism, and the associated practices related to risk management, are enacted in order to reduce loss and to prevent, spread and minimize risk. Moreover, such strategies may be linked with other techniques that are designed in order to promote a particular image, or profile, of mass private spaces.Dept. of Sociology and Anthropology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .H88. Source: Masters Abstracts International, Volume: 42-03, page: 0830. Adviser: Daniel O'Connor. Thesis (M.A.)--University of Windsor (Canada), 2003.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".