Transdisciplinary solutions to methodological and ethical problems in performing arts psychology
Bibliographic record
Abstract
In the first part of this article, I argue that transdisciplinary research can help address both the WEIRD problem of homogenous samples and the categorical problem of overgeneralized practice conceptions in performing arts psychology. Like other areas of performance science, performing arts psychology engages with practices and practice-based knowledge that are studied differently by subject-specific disciplines. I propose a transdisciplinary research model that facilitates greater overlap and transfer between the scientific, subject-specific, and practice-based forms of research and knowledge that surround a practice. The potential benefits of such a model are more inclusive samples, diversified methods, grounded research questions, and widely applicable results. The problems mentioned above are also ethical. Psychological definitions of performance that derive from overgeneralized conceptions and overreliance on homogenous samples are transferred to diverse peoples, practices, and contexts as general knowledge. This fails to apply principles of equity and relational ethics, which in turn reveals some limitations of established ethics procedures. In the second section of this article, I therefore revisit my argument for transdisciplinary research, now with a focus on the triad of research ethics that is brought into a transdisciplinary project through the different priorities of scientific, subject-specific, and practice-based research domains; namely, procedural ethics, relational ethics, and principles of equity. Transdisciplinary researchers are not only negotiating across methodological paradigms that determine research validity, they are also negotiating across ethical values. Combining the two sections of the article, I argue that the challenge of negotiation can be flipped into a solution to the WEIRD and practice conception problems in performing arts psychology. I argue that whereas critical calls for radical departures were needed to identify these problems, solutions are available in bridges between different ethical and methodological approaches.
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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 | 0.414 | 0.345 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.160 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.010 | 0.042 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".