Bibliographic record
Abstract
In today's world, towns and cities dynamically develop over time and that's why urban regeneration is a widely experienced phenomenon. How can Business Improvement Districts (BIDs) create necessary conditions for the development of these phenomena? What is the role that BIDs have in entrepreneurial urbanism, supporting SMEs, city marketing and city branding? These are questions examined in this volume, in an effort to provide an extensive analysis of business improvement districts. Enriched with an analysis of various case studies, including South Africa, Ontario, Tokyo, Barcelona, Slovenia and with an in-field analysis of a cultural heritage site, Korca, Albania, the book analyses the importance, benefits, and impacts of this kind of organization. It highlights the social, economic and ecologic challenges to the historic city markets today, which led to their rapid stagnancy. This book offers a practical and structured guide of the concept of Business Improvement Districts and highlights the best practices for management, financing and organizing. It sheds light on the impacts and benefits of business improvement districts, offering conclusions about their influence on the future improvement of cultural and urban sites. It will be of value to researchers, academics, professionals, and students in the fields of management, organizational studies, strategy, and sustainable development of tourism districts.
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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 | 0.000 | 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) | 0.010 | 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".