The rhetoric and reality of socially engaged arts projects delivered by cultural mega events: the case of Hull UKCoC 2017
Bibliographic record
Abstract
This thesis examines the intersection of two cultural phenomena: socially engaged arts (SEA) and cultural mega events (CME). It analyses several cases where these phenomena have interconnected and explores the resulting tensions. Both phenomena are present in cultural policies (Green, 2019), the funding strategies of national arts organisations (ACE, 2020), as well as local and national government agendas (DCMS, 2009; Hull City Council, 2018). Though they appear distinctly different at first glance, both SEA and CME are part of the contemporary arts ecology and are employed as a means to engaging new audiences. This research aims to develop a better understanding of the influences that CME and SEA have on each other, and the potential benefits and challenges of combining these two phenomena in cultural policy.Despite their differences, when CME and SEA are brought together, they form a hybridised (Harvey, 2013) aspect of the arts environment. This thesis provides an in-depth analysis of the two cultural phenomena and their overlapping history, particularly contextualised through the Hull2017 UK City of Culture (UKCoC). The intention has been to establish a nuanced understanding of the effects of CME on SEA, and potential opportunities for the arts in future.In order to verify Hull2017’s claim to have achieved significant community engagement through the Land of Green Ginger (LoGG) project, this research compares its legacy to that of another less well-funded Hull2017 project, Back to Ours (BtO).The questions addressed in this research include the history of SEA in Hull, whether Hull had a precedent of arts organisations working in non-traditional spaces, and if Hull2017 should have collaborated more with local arts groups/artists to better embed their cultural programme in the lives of local communities. Although SEA and CME represent distinct approaches to creating cultural offerings, they can collaborate effectively if mutual benefits are identified. Lastly, the thesis considers the potential risks facing both CME and SEA due to the cost of living crisis in the UK, as arts funding is often reduced during economic downturns. Collaboration may help secure the future of CME but should not be seen as the sole path forward for SEA, which must remain critical of State-led agendas while continuing to innovate. SEA should maintain its practice rooted in challenging established patterns and exploring new methods.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.040 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".