Introduction: The Performative Force of Practice-Based Research
Bibliographic record
Abstract
As any artist, researcher, teacher, athlete, or home cook knows, one often discovers what is most enriching and affirming in one's practice when it is disrupted or curtailed in some manner.Likewise, adaptations made in response to such circumstances, however pragmatic, temporary, or instrumentalist, can frequently lead to a fundamental rethinking of some of the core concepts and tenets of a practice.For many of us, COVID-19 was just such a reckoning.It forced us to pivot radically in our personal and professional lives; while this has undoubtedly affected our individual approaches to all that we do, as the contributions to this special double issue of Performance Matters attest, for many of us, the pandemic has also strengthened and reaffirmed our attachments to our communities of practice.Indeed, COVID had much to do with the genesis of this volume.More specifically, it builds on a two-day international summit on practice-based research (PBR) organized by Ellen Waterman and Nina Sun Eidsheim and held online in the summer of 2021. 1 Thirty-six artists/scholars held a series of thematic conversations exploring the opportunities, challenges, and exciting uncharted territory of PBR through four broad nodes: knowledge, power, ethics, and affect.As part of a collective writing exercise on Google Docs that concluded the summit, and that explored possible takeaways and next steps for participants, Peter Dickinson volunteered this journal as a venue to build on the energy of the summit through a PBR-themed issue.Ellen and Peter duly drafted a call for papers inviting artists/scholars working in/with PBR to expand on the 2021 summit nodes, or to introduce new ones through a range of artistic media and writing.In doing so, we were especially eager to marry the inter-, multi-, and transdisciplinary methods of PBR with the journal's particular focus on the materiality and consequentiality of performance-that is, what PBR does and why it is meaningful.Hence our foundational question for the issue: What is the performative force of practice-based research?For example, what exactly is produced when universal design principles are explored through music, when intergenerational trauma is examined through dance, or when performance art is used to probe the effects of climate change?The darker meaning that "performative" took on during the pandemic also lurks beneath such questions.While we remain committed to the Austinian sense of the citational performative (whether linguistic or artistic) as "doing something" in the world (Austin 1962), several of the papers in this issue critique moments when the performative becomes an empty form of virtue signalling.Whether it is termed practice-based or practice-led research, practice-as-research, research-creation, or simply artistic research, the underlying proposition of the various methodologies we here call PBR is that creative practices may be used to seek out knowledge while also challenging the epistemological assumptions that produce the concept "research."In other words, creative practices such as music,
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".