Musical Moments That Matter: Is a Multicultural Human Subject Possible?
Bibliographic record
Abstract
One of the strongest memories I hold of my musical experiences as an adolescent was during the turbulent 1960s.I was living in Knoxville, Tennessee, at the time.I recall that my high school choral conductor had arranged a medley of protest songs from the Civil Rights movement to honour Dr. Martin Luther King shortly after his assassination.However, after receiving complaints from some parents that the songs were "too political," the school principal prohibited us from performing them in concert.A few of us, however, stood on stage at the end of the concert and sang the medley anyway as an impromptu encore, much to the thrill of the conductor and the chagrin of the principal who had issued the ban.I am curious about why this particular event remains with me: the power I felt in that moment on stage, singing songs of political protest, voicing my own personal protest against the oppressive school administration's attempt to silence our songs of solidarity.Now, so many years later, as a choral music educator my interests lie in world music and antiracism education.In Music and EvenJday Life, DeN ora (2000) posits that the
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".