Bibliographic record
Abstract
The representation of women in opera, both on and off stage, has been an issue for all of opera’s 400-year history. In her now famous book Opera, or, the Undoing of Women (1979), Catherine Clement was one of the first to bring feminist theory to bear on opera scholarship, revealing true problems in the representation of operatic women on the stage in opera’s canon. But as I explore in this project, the same can be said for opera’s women behind the scenes. According to 2018 stats from OPERA America, out of the 786 total leadership positions held in North American houses, only 34.5% of administrative roles were occupied by women. In this proposed poster presentation, I will analyze the data from 1990 to the present with respect to the lack of gender parity in the opera industry in North America. I will also report on a series of interviews I conducted with several prominent female-identifying leaders in the opera industry, revealing currents in their narratives, and lessons for future women in this field. I will use this data to outline possible steps towards a more equitable opera industry. As Francesca Zambello, the most distinguished female director/general director in North America today, shared in a recent speech: “Opera needs truly excellent leadership to thrive, and when women are discouraged, it halves our pool of possibilities. We need leadership that is representative of the diverse audience we have and hope to grow.”
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".