Bibliographic record
Abstract
In this article, Music-in-Becoming, I explore the integrative practices of composing music and researching its emergence. The article reports from an artistic research project on composition as lived experience. The music that serves as the raw material for my inquiry consists of four pieces for three overtone-skilled singers and one double bass player for the concert Ørenslyd, all performed in Berlin in October 2019. My intention is to show how composing music takes place in a phenomenological in-between of composer and work that is driven by attentive listening and the complementary skills of activeness and receptiveness. In this in-between, the skill of listening plays a crucial role as it is both participatory and receptive. I further examine and discuss the methods I use during this exploration, influenced by both artistic research and phenomenological research. The two methodological approaches are compared regarding their characteristic differences and their potential kinships. My presumption is that the active-receptive act of composing at its core is a phenomenological-hermeneutic practice. I conclude that artistic research seems to be profoundly influenced by phenomenology, whereas not all phenomenological research is influenced by art as practices or as a research approach.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".