Bibliographic record
Abstract
Abstract Higher education institutions potentially act as sites of gender-based inequity and discrimination against women. This chapter draws on several discrepancies between existing literature on feminist theories in music education and their application in higher music education. Drawing on comprehensive literature, it investigates how scholars engage with these theories and how they were deployed within institutional contexts. Through a critical feminist perspective and analytical review, I argue the ways that these inequities are deeply embedded in hierarchical structures, and feminist theories have yet to significantly influence practices in higher music education. Missing are cohesive and preventive strategies to address structural gender-based inequities. Moreover, women’s lived experiences within the discipline remain insufficiently explored. While the literature is extensively dominated by debates over understanding factors and possibilities that have perpetuated gender-based inequity, I argue that while critiquing the political power structures and hierarchies through radical feminist theories is essential, the profession should galvanize its forces to narratively examine women’s lived experiences within higher music education contexts. Since feminist theories can best be judged by the impact they make on the equitable practice of music education, the profession might be able to align more deeply with feminist theories. Thus, despite scholars’ theorizing, given that gender-based inequities persist in music education higher education, the scope of feminist theorizing has fallen short.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".