Light Night: an “enlightening” place marketing experience
Bibliographic record
Abstract
Purpose The Light Night initiative currently under development in the UK offers an innovative approach to revitalising town and city centres by involving residents and visitors through culture and the arts. This initiative is based on the successful Nuit Blanche (White Night) culture‐led urban revitalisation model tested in cities like Brussels, Madrid, Montreal, Rome, São Paulo, Skopje and Toronto, where the Nuit Blanche programme alone resulted in an estimated $4.9 million economic impact on the city and attracted 800,000 people (Toronto). This paper aims to review elements of good practice internationally in the implementation of this concept and suggest a way forward for its realisation in the UK. Design/methodology/approach This case study builds on the 24‐hour city concept and provides a practical example of how culture‐led urban revitalisation through the Light Night programme can be implemented in Birmingham, Leeds, Liverpool, Nottingham and Sheffield based on successful international experiences. Findings Evidence gathered through a review of the literature provided in this case study would suggest that community involvement through creative culture‐led urban revitalisation initiatives can be instrumental in developing sustainable places and communities. Furthermore, it may provide a pathway towards overcoming some of the negative perceptions associated with the night‐time economy in many British town and city centres. The Light Night programme provides an opportunity for key social stakeholders to implement this through its focus on shared culture, history and identity. Originality/value This paper reviews the evolution of the European Nuit Blanche initiative using a case study approach, and its interpretation in the UK through the Light Night programme. It further explores the economic, cultural and social benefits of Light Nights and Nuit Blanche on community cohesion, tourism and regeneration. This paper is of value to practitioners and policy‐makers in place marketing, town centre management, local authority economic development officers, business managers, urban regeneration consultants, academics, tourism officers, community leaders and town centre residents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".