Bibliographic record
Abstract
Two years ago, I created a performance piece called Singing Lessons: A Performance about Learning to Sing, in which I tried to demonstrate what I had learned about singing and about life from six remarkable singing teachers.In it, I told the story of my quest for voice, using archival audiotapes and photographs, and by telling stories, by demonstrating some technical aspects of singing, and by singing repertoire I had studied with each teacher.Mary Clare Powell (2002), in a chapter titled "Why I send the poet to teach my courses" explores how being a poet influences her university teaching.It made me think of how being a singer shapes my teaching.This paper is the result.I begin with a segment of the Singing Lessons performance piece which includes the script, photographs, and video clips of the sung material.1 This ten minute long segment takes place approximately in the middle of the original hourlong performance.At the end of the Singing Lessons segment, I turn to a reflection on what it means to take my singer to teach my courses. The Script: Singing LessonsAldeburgh is a fishing village on the Suffolk coast of England.The North Sea pounds the shingle shore 2 and the smells of salt sea and freshly caught fish and chips wrapped in newspaper always mingle on that beach.Benjamin Britten, the composer, and Peter Pears, the tenor, lived together in Aldeburgh, and it was there that most of Britten's music was premiered.When I was 27, I went on scholarship to study at the 1 The accompanist is Mark Sirett.2 A shingle shore is a beach composed of small rounded pebbles.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".