But If It’s Not Music, What Is It? Defining Interstitial Artistic Practices
Bibliographic record
Abstract
We often think of art as structured in art worlds. But what lies between art worlds? Do we find spaces of undefined practices, or are interstitial practices also clearly defined? For this text, we interviewed 11 French artists with sound-based practices who situate themselves in the spaces where music overlaps with the visual arts, instrument building, performance art, and poetry. How do these artists define themselves, given that the available labels do not satisfy them? How do they actually use established categories when they do not want to make their artistic practice conform to them? Three positioning strategies came up recurrently in our interviews: so-called negative, additive, and generic definitional strategies. We show here that each of these definitional strategies implies related artistic choices, which make it possible for these artists to both legitimize their sense of belonging to an interstitial space and to use this belonging as a powerful means to develop a unique identity. Beyond a simple, sometimes superficial, refusal to be labeled, the strategies we identify appear to be the true markers of “interstitial” artists and their careers within and around art worlds.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".