Bibliographic record
Abstract
Teaching Difficult Topics provides a series of on-the-ground reflections from college music instructors working in a wide variety of institutional settings about their approaches to inclusive, supportive pedagogy in the music classroom. Although some imagine the music classroom to be an apolitical space, instructors find themselves increasingly in need of resources for incorporating issues of race and ethnicity, gender and sexuality, and historical trauma into their classrooms in ways that support student learning and safeguard their classroom communities. The teaching reflections in Teaching Difficult Topics examine difficult themes that fall into three primary categories: subjects that instructors sense to be controversial or emotionally challenging to discuss, those that derive from or intersect with real-world events that are difficult to process, and bigger-picture discussions of how music studies often focuses on dominant narratives while overlooking other perspectives. Some chapters offer practical guidance, lesson plans, and teaching materials to enable instructors to build discussions of race, gender, sexuality, and traumatic histories into their own classrooms; others take a more global view, reflecting on the importance and relevance of teaching these difficult topics and on how to respond in the music classroom when external events disrupt daily life.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.020 |
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".