Municipal Corporate Security, Legal Knowledges, and the Urban Problem Space
Bibliographic record
Abstract
Previous sociolegal research has neglected how the work of corporate security agents is enabled and constrained by legal knowledges. This article explores how legal knowledges shape the work of municipal corporate security (MCS) agents in Canadian cities. MCS offices are a new development in municipal governments. By drawing on analysis of freedom of information requests and interviews with MCS managers and staff, we investigate how legal knowledges shape MCS practices in Canadian cities, with a focus on trespass law, licensing law, litigation, labor law, privacy law, and workplace violence legislation that converge in MCS offices. MCS agents must interpret, translate, and apply these laws in their municipal jurisdiction or urban problem space to confront the defining element of the urban milieu—nuisance—but also to mitigate the risks. Interpretation and use of numerous laws by MCS staff constitute a distinctively urban way of governing through legal knowledge.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.040 | 0.086 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".