Attuning to the Interstices of Arts-Based Research and the Expressive Arts: An Experiment in Expanding the Possibilities for Creative Approaches to Inquiry
Bibliographic record
Abstract
In this article, the authors2 examine the generative, yet heretofore under-articulated convergences and divergences between the eld of expressive arts (EXA) and the sub-genre of arts-based research known as a/r/tography. Experimenting with the discursive and practical terrain between the two elds, the authors discuss what they see and sense as the potentiality for an EXA-informed variant of a/r/tographic research informed by new materialist theoretical perspectives. Overall, the work aims to contribute to the expanding dialogue among arts-based researchers who are reaching across diverse discursive and disciplinary boundaries, mining relevant conceptual and practical linkages for thinking the role of creative making practices in social and educational inquiry anew.
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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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".