Practice-led Research, Research-led Practice in the Creative Arts
Bibliographic record
Abstract
This book addresses one of the most exciting and innovative developments within higher education: the rise in prominence of the creative arts and the accelerating recognition that creative practice is a form of research. The book considers how creative practice can lead to research insights through what is often known as practice-led research. But unlike other books on practice-led research, it balances this with discussion of how research can impact positively on creative practice through research-led practice. The editors posit an iterative and web-like relationship between practice and research. Essays within the book cover a wide range of disciplines including creative writing, dance, music, theatre, film and new media, and the contributors are from the UK, US, Canada and Australia. The subject is approached from numerous angles: the authors discuss methodologies of practice-led research and research-led practice, their own creative work as a form of research, research training for creative practitioners, and the politics and histories of practice-led research and research-led practice within the university. The book will be invaluable for creative practitioners, researchers, students in the creative arts and university leaders. Key Features The first book to document, conceptualise and analyse practice-led research in the creative arts and to balance it with research-led practice Written by highly qualified academics and practitioners across the creative arts and sciences Brings together empirical, cultural and creative approaches Presents illuminating case histories of creative work and practice-led research More information about Hazel Smith More information about Roger T. Dean
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".