‘Clean and safe’ passage: Business Improvement Districts, urban security modes, and knowledge brokers
Bibliographic record
Abstract
This paper interrogates the complex role of Business Improvement Districts (BIDs) in securing and shaping conduct in public retail and ‘entertainment’ spaces in Canadian cities. Adopting a Foucault-inspired sociology of governance perspective, this paper uncovers key features of the role of BIDs therein and casts doubt upon assumptions evident in previous research, including in relation to urban neo-liberalism. BIDs seek to exclude obstacles, which include ‘panhandlers’ and the homeless, from public spaces. Yet, other barriers are placed into relief by a proliferating ‘clean and safe’ rationality and are deemed to interfere with consumption conduct and pedestrian flow. These include BID members engaged in moralized enterprises. Some BIDs are deploying CCTV surveillance arrangements and interactive ‘ambassadors’ consistent with ‘clean and safe’, whereas others avoid these modes and rely upon and lobby for public sources. The role of BID coordinators in brokering specialized knowledge is pivotal in these varied security arrangements. Theoretical implications of this analysis are discussed.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".