Fifty years of Business Improvement Districts: A reappraisal of the dominant perspectives and debates
Bibliographic record
Abstract
Originally created in 1970 by a small group of business people in Toronto’s Bloor West Village, Business Improvement Districts (hereafter BIDs) have become commonplace urban revitalisation strategies in cities across the world. Many critical urban scholars have conceptualised BIDs as neoliberal organisations and have resultantly critiqued their role in contemporary urban governance. With BIDs now existing for over 50 years, the purpose of this paper is to provide an overdue reappraisal of the BID research and orient future scholarship. After describing key debates from early BID research, this paper analyses two distinct themes in more recent scholarship: (1) BID policy mobility, and (2) BIDs and social regulation. As the BID model has been transferred to new locations across both the Global North and South, its rapid mobility demonstrates the permeability, resilience and limits of neoliberal urban policies. Moreover, BIDs’ social control tactics highlight how these organisations are shaped by a neoliberal logic that seeks to manage and control urban spaces in ways that attract desirable consumers and exclude the visible poor. This paper outlines the origins of both bodies of work and traces common patterns and variances over time. It concludes by highlighting gaps in the existing literature and offers suggestions for future work.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".