Bibliographic record
Abstract
From Scarabea, Artusa’s old nurse in Francesco Mannelli’s La Maga Fuminata (1638 Venice) through the Canadian nursing sisters in Stephanie Martin’s Llandovery Castle (2018 Toronto), over one hundred nurse characters appear in opera roles ranging from silent cast extra to principal singer.The Nurse in History and Opera: From Servant to Sisterexplores that role over the span of opera’s existence. Judith Barger examines the nurse character in opera within the sociohistorical context of her real-life counterparts off stage; the progression of the nurse from servant to sister, both inside and outside the opera house, is a commentary on how society has viewed its women. The book then discusses textual and musical interactions between opera’s nurses and other characters with attention to what the nurse’s role contributes to opera in six categories identified as common to opera’s nurses – Comic, Former, Knowledgeable, Motherly, Scheming, and Specialized. When viewed through the lens of social history, opera’s nurse characters merit attention for the glimpse that they offer of a unique musical and dramatic journey from servant to sister, and for the commentary that they offer on women’s perceived place and status not only on the opera stage, but in society as well. View the author’s website here:Judith Barger | Author, Nurse Historian & Music Scholar
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".