Bibliographic record
Abstract
How did composers and performers use the lost art of pantomime to explore and promote the Enlightenment ideals of free expression? This book explains the relationships between music, pantomime and freedom in pre-Revolutionary France. It argues that composers and performers recognized their agency when they attempted, from the 1730s through the end of the OldRegime, to revive a lost art called 'pantomime' for their compositions. In musical settings of pantomimes in French operas and instrumental works, leading composers of the time - Rameau, Rousseau, Gluck, and Salieri - used pantomime as a type of expressive dance and acting style that marked an aesthetic rupture between Louis XIV's absolutist governance and the Enlightenment ideals of free expression. In musical settings of pantomime, these composers cultivated various forms of freedom theorized in Enlightenment writings: artistic freedom for the composer; freedom as self-governance; interpretive freedom for spectators; freedom of action for performers; and freedom from dance convention. Thus, pantomime was not only a dance genre; it also functioned as an expressive medium for top performers and invited spectators to draw their own interpretative conclusions. Placing the cultural phenomenon of pantomime in the intellectual context of the Enlightenment, the book explains how composers helped develop thinking and feeling subjects in pre-Revolutionary France. HEDY LAW is Associate Professor of Musicology at the University of British Columbia, Vancouver.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".