Bibliographic record
Abstract
A FancyMy foray into arts-based educational research has taken a somewhat circuitous path.In retrospect, it seems that I have been writing in the realm of arts-based educational research for the past ten years, without knowing that I was either thinking or acting like an arts-based researcher.One of my doctoral students invited me to write an article with her based on my reaction to a journal article she had given me to read a few years ago (Gouzouasis & Lee, 2002).She always told me that I sounded like an artist-researcher, especially when I shared my lengthy, colorful, musician stories during our discussions regarding her dissertation.I always considered myself to have had strong training in various qualitative forms of music research-aesthetics, music history and music theory.And although I was a professional singer in "a prior lifetime," outside of writing lyrics for songs, I was never encouraged to write freely and creatively by my mentors.As I became familiar with some of the arts-based educational research literature, I noticed one peculiar thing.Because there is such an emphasis on language arts, and an emergence of arts-based research in visual arts (Irwin & deCosson 2002; Springgay, Irwin, Leggo, & Gouzouasis, 2007), I wondered how one goes about creating music and "thinking musically" in an arts-based research context.To elaborate, I became interested in the music endeavors of musicians-how music
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".