Bibliographic record
Abstract
Edinburgh has long been recognized as a global model for “festival cities”, and is often cited as a leader in the planning and evaluation of events. In 2010 Edinburgh won the World Festival and Event City award from the International Festival and Event Association (IFEA) and was declared the most outstanding global entry. According to Visit Scotland’s 2015 visitor survey (cited in BOP Consulting 2018, p. 8), “Edinburgh’s Festivals each year deliver over 3,000 events, reaching audiences of more than 4.5 million and creating the equivalent of approximately 6,000 full time jobs. 32% of the 14 million+ annual visitors to Scotland are moti- vated by the nation’s cultural and heritage offer, in which the Festivals play a defining role.” The city is frequently cited in the events literature, and its generous posting of material online is a boon to scholars and practitioners alike. The companion book in this series, Event Impact Assessment (Getz, 2019), presents highlights from a succession of impact studies that Festivals Edinburgh has placed online, while in this book we examine portfolio management through a review of published documents (all available online) and input from Festivals Edinburgh. Permanent, formal stakeholder collaboration, and strategic planning sup- ported by research is in large part what distinguishes Edinburgh’s event port- folio. The Festivals Forum (established in 2007 following the first Thundering Hooves report) facilitates stakeholder collaboration, particularly by bringing major funders to the table with events and venues. Festivals Edinburgh is a formal, staffed association of the eleven major, permanent festivals that contrib- ute most to the city’s image and to event-tourism impacts. Leadership is shared, not concentrated in one organization. As well, the city and Scottish Government work closely together, facilitated by the explicit portfolio strategy followed by EventScotland. Innovation in programming the festivals is matched by leadership in envi- ronmental sustainability and social responsibility. Engagement with residents is considered to be a high priority, and this includes demonstrating benefits through regular and comprehensive impact studies that cover cultural, social, economic and environmental impacts. Investment in venues and infrastructure has also been a priority for the city.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.017 |
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".