‘The right people to do the right job … ’: Legitimation work of municipal corporate security personnel
Bibliographic record
Abstract
Corporate security units have emerged in municipal governments across North America. They resemble corporate security units found in private corporations, yet they are publically funded. Presently little is known about how the work of municipal corporate security units differs from that of public police, private contract security, or corporate security in the private sector. Though previous research has examined attitudes of public police toward private contract security, and vice versa, corporate security attitudes have been overlooked, as has how public sector corporate security personnel compare themselves to their counterparts in private corporations. This article extends analyses of ‘legitimation work’ of security and policing agents by examining what MCS personnel claim about public police, private corporate security, and private contract security. We show that MCS personnel claim the public police officers have a different skill set and possess limitations that MCS units overcome; that private corporate security is said to be driven more by a profit motive and is less accountable; and that private contract security agents have less expertise and are of lesser value than MCS personnel. Finally, we explore implications of this study for future research in policing and security.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".