Bibliographic record
Abstract
their gestures. n conducting workshops, attention is sometimes drawn to the relationship between conducting and the art of mime theatre. At some, professional mimes lecture on fundamentals and provide feedback on participants' conduct ing. Although I always found mime concepts compelling, my attempts to transfer them to my own conducting were frustrated by a very superficial understanding of the art form. This frustration led me to study mime theatre myself, hoping to develop an understanding that would enhance my own conducting and teaching. My brief studies have taught me that mime can assist us both physically and artistically as we strive to bring clarity, economy, and expression to our musical leadership. With the help of mime techniques, we can talk less and conduct more, allowing our students to make better music. There are some obvious parallels between the two art forms. Mimes and conductors both refine silent techniques to maximize clarity and optimize their message. There is a great deal of similar vocabulary: Mimes talk about phrasing, unison, counterpoint, and canon. They also deal with issues of pacing, shape, style, articulation, and dynamics many of the same things that define and ani mate music. What follows is a selection of mime concepts that have inspired the most reflection on my approach to conducting.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".