Bibliographic record
Abstract
In her landmark book, In Search of Music Education (University of Illinois Press, 1997), Estelle R. Jorgensen lays the groundwork for the philosophy of music education, of which she is today’s foremost proponent. Decidedly not a “how-to” manual, her book poses difficult questions undergirding a systematic reflection on, first, the nature of education (Chapter 1); the nature of music (Chapter 2), and the dialectics and dialogics of music education (Chapter 3), reconciling the tensions and ambiguities when music and education are combined as an autonomous yet porous discipline. Jorgensen cites John Dewey, Paulo Freire, Maxine Greene, Susanne Langer, Israel Scheffler, and Alfred North Whitehead as her philosophical mentors, but it is Aristotle who is foundational to her analytic method. My chapter offers a close reading of In Search of Music Education within the parameters of its Hellenistic roots, specifically Jorgensen’s penchant for taxonomical structures, her embrace of Gaia as the hypothesis of universal interconnectedness, and her version of the ancients’ conception of mousikē technē, that is, the practice of music as aligned with the humanities. The chapter elaborates salient interrelationships between tenets of Greek aesthetic/poetic/cultural theory and Jorgensen’s attention to such contemporary educational values as interdisciplinarity and education for the common good.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".