The role of quarters in large city centres: a Mancunian case study
Bibliographic record
Abstract
Purpose The purpose of the paper is to evaluate the development, role and management of quarters in UK cities. Design/methodology/approach A case study based on Manchester's Northern Quarter, using secondary documentary materials and semi‐structured interviews with urban managers and residents of the Quarter. Findings The emergence of the Northern Quarter is a relatively recent phenomenon, with small scale cultural industries and artists moving into take advantage of cheap property following the collapse of the area's economic base in the 1970s. Its branding was a development of the 1990s, set within the wider context of the marketing of the city as a whole. The area has regenerated, but its idiosyncratic character is continuously under pressure from developers and the demands of corporate retailing/leisure, from which it needs to be protected as far as possible. It is not an appropriate area for a business improvement district, but rather needs treating as an eco‐system and allowed to develop under its own momentum. Research limitations/implications This is a single case study, which would merit duplication in other cities. Practical implications The paper suggests that “real” quarters are essentially organic in their origins, and cannot be planned or managed in a top‐down way. The serial replication of artificial quarters will not assist the differentiation of localities in increasingly competitive place markets. Originality/value The paper will be of interest to students and practitioners of urban place management.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".