“What Happens When…?”: A Meditation on Experimentation and Communication in Practices of Artistic Research
Bibliographic record
Abstract
Natalia Esling’s introductory article to this special issue of CTR offers a snapshot of views and experiences of an international colleague (Falk Hübner) as a point of comparison for her own experiences using practice-based research (PBR) methodologies to examine the impact of sensory manipulation in contemporary performance. Discussing in brief her own research methods and experiment design, she considers several fundamental criteria associated with Artistic Research (AR)—process orientation, knowledge generation, and utility/transferability—arguing that the significance of discoveries made and knowledges gained through practices of AR lies in the capacity to communicate those discoveries and knowledges across multi- and interdisciplinary boundaries. The article further articulates the benefit of hands-on processes that lead to more incisive and precise questions related to discrete aspects of the dynamics of performance; it considers a way of thinking about AR in relation to performances/productions that does not necessarily privilege a performance/production as a final “outcome,” but rather that positions it as one aspect within the broader process of addressing a particular question through AR.
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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".