ROLE HISTORIC ASSETS CAN PLAY IN REVIVING THE RETAIL HIGH STREET: A CASE STUDY OF DERBY’S RETAIL HIGH STREET
Bibliographic record
Abstract
The long-term decline in the historic high street has been an important issue for local communities, governments and real estate investors. This has led to significant discussions concerning the triggers of retail decline and consideration for how heritage themed high streets can evolve in the future and the associated resources for this to be achieved. Utilising a case study of Derby's historical Cathedral Quarter, this paper explores (i) the issues involved in reversing the decline of retail in the traditional high street; (ii) the strategies used to sustain and improve the high street as a destination; and (iii) the role of heritage assets in improving business occupancy of high street premises. In order to provide an insight into the processes available to regenerate the high street and attract space occupiers, a series of semi-structured interviews were undertaken with leading real estate consultants and investment professionals. The research findings suggest there are multiple reasons for the decline, including economic, environmental, and functional factors, and that these will continue to impact the evolution of the high street moving forward, unless proactive strategies are put in place. Increased mixed-use development within town centres, including residential and co-working space, is seen by interviewees as significant. Furthermore, the case study provides clear evidence that utilising heritage assets with unique characteristics can positively impact the retail high street. This can include reinstating historical frontages of retail units and so strengthening visiting numbers that creates destination footfall, resulting not only in a decrease in vacancy rates but often improved rental values. A catalyst is to provide an informed strategy to those wishing to undertake projects similar to the funded regeneration works on the historical assets within Derby's Cathedral Quarter Scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".