Bibliographic record
Abstract
<JATS1:p>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</JATS1:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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; both teacher heads agree on what is shown here.
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".