Bibliographic record
Abstract
Abstract This reflective and historical essay nominally responds to the question: What does “feminism” potentially mean in relation to music teaching and learning? From the time this particular feminist began teaching music in public schools in 1974 and until her retirement in 2016, Roberta Lamb enacted and embodied some such potentials; therefore, it strikes her as disconnected to be considering “potentials” in 2022. Amy Fay published her memoir, Music Study in Germany, in 1880; Sophie Drinker, Music and Women: The Story of Women in Their Relation to Music, in 1948. Consequently, the question of women, feminism, and music teaching and learning is an old one. Even so, it is still considered novel in music education. Why? “The general field of education and the specific discipline of musicology have been more willing to interact with feminist theory in music education than has music education itself” (Lamb, 1993–1994, p. 5). Reviewing the past 100 years, generally, and the past 50, specifically within the context of one career, provides some contextual suggestions, and celebrates where we have been, where we are, and where we might be headed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".