Filling the voids: The role of business improvement districts in retail attraction
Bibliographic record
Abstract
In light of the voids that exist in the leasing ecosystem as a result of the incentive structures within which private sector actors operate, business improvement districts (BIDs) can — and in many cases, should — play a more proactive role in retail attraction, working closely with property owners and leasing agents to catalyse, elevate, shape and/or nurture the retail offer, not just for the benefit of the district as a whole but also the bottom-line interests of those same actors. With some notable exceptions, this sort of work has been largely confined to the US and would represent a significant shift in the UK context, though arguably one that makes a great deal of sense, given not only that the high street continues to struggle, but also that BIDs, not local authorities, are in many ways better positioned to spearhead and front such efforts. Drawing on observations and case studies from the author’s nearly 25 years of consulting work across the US, Canada and the UK, this paper carefully lays out the argument(s) in favour of a BID undertaking such an initiative, delves into the practical considerations that should inform whether to do so, details the specific functions it would entail, and discusses the changes in mindset it would demand. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".