Pedagogical ideas on sonic, mediated, and virtual musical landscapes: Teaching hip hop in a university classroom
Bibliographic record
Abstract
Based on the experience of teaching the history of American hip hop music to a classroom of Canadian university students, the author considers the disjuncture between the cultural orientations of herself and her students. The author considers teaching methods to solve the place-based disjuncture that often occurs when teaching genres such as hip hop, as well as in the teaching of mediated and virtual musics. She draws upon the fields of ethnomusicology and popular music to consider solutions that examine the relationships between the performers and listeners of hip hop music and the mediators that are involved in the process of negotiating the space that is created in the production and consumption of music and who have the greatest influence on how the music is heard by the listeners. This article presents several ideas on how cultural specificity can be dealt with when studying or teaching about a musical system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".