Arts, Culture and Community Development
Bibliographic record
Abstract
This edited collection profiles the sites and subjects of arts practices in different geographical contexts, including Hong Kong and mainland China, India and Sri Lanka, Finland, Chile, Brazil, Lebanon, Mexico, the USA, Germany, Canada, the UK, and Ireland. Chapters capture how collective hopes, fears, allegiances, frustrations, and memories, are sung, danced, played, etched on walls, or conveyed through puppets and theatre. Contributors to the volume thus draw attention to some of the diverse ways that groups of people collectively make sense of, re-imagine or seek to change the personal, cultural, social, economic, political, or territorial conditions of their lives, while using the arts as their means and spaces of engagement. Across its chapters, the book explores a number of broad themes and questions. How can we conceptualise the relationship between community development and arts/cultural practice? What diverse forms does this relationship take in contemporary contexts? How do communities of people engage with, utilise, make sense of and through particular artforms and media? How can we understand the aesthetic and associated meanings of such engagements? How are the power dynamics related to authorship, resources, public recognition, and expectations of impact negotiated within community-based arts processes? How do economistic and neoliberal rationalities influence arts processes and programmes in community contexts? Together, the chapters also critically interrogate if, and how, dominant rationalities are being resisted and challenged through arts practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".