Arts-based Methods in Socially Engaged Research Practice: A Classification Framework
Bibliographic record
Abstract
Arts-based research has recently gained an increasing popularity within qualitative inquiry. It is applied in various disciplines, including health, psychology, education, and anthropology. Arts-based research uses artistic forms and expressions to explore, understand, represent, and even challenge human experiences. In this paper we aim to create order in the messy field of artistically inspired methods of socially engaged research. We review literature to establish study and distinguished three major categories for classifying arts-based research: research about art, art as research, and art in research. We further identify five main forms of arts-based research: visual art, sound art, literary art, performing art, and new media. Relevant examples of socially engaged research are provided to illustrate how different artistic methods are used within the forms identified. This classification framework provides artists and researchers a general introduction to arts-based research and helps them to better position themselves and their projects in a field in full development.
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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 | 0.063 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".