Bibliographic record
Abstract
Improvisation can be perceived as a great secret of life itself, an unattainable artistic practice, or perhaps just as an easy way out when something goes wrong. But is there more? And how can improvisation also be a way in, for the artist, for the researcher, and a disposition for living? This article unfolds as a duoethnographic journey of improvisation in music and dance. In the dialogues between two researcher-artists—one coming from the subject of music and the other from dance—discussions arise about another that inspire the other's history and lifts what their stories affect in art education, education, and research in general. In the article, we investigate how different framings of improvisational encounters might make space where space is not always obvious. We touch on different ways of understanding improvisation and how improvisation can connect with life in a broader sense, holding on to Fischlin et al's statement that “improvisation matters” (243). Could it be that improvisation is something "more" than what we now give it credit for? Are we longing for an improvisation pedagogy for life itself? Through such questions, we offer thoughts on how pedagogy and life are entangled, and how longing for more improvisation might lead us in and out of teaching, artistic co-creation, and situations in life to provide space for inclusion.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.106 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".