Spotlight on Music Collections: Interview with Marion Newman, (Ne'ega,) Mezzo-Soprano
Bibliographic record
Abstract
While previous Spotlight columns have focused on music collections and archives in Canada through the voices of those who work with them, this interview takes a slightly different angle by focusing on the career of Mezzo Soprano Marion Newman (Nege’ga), an internationally recognized opera singer who will be joining the University of Victoria School of Music as Assistant Professor on July 1, 2024. Newman is known across Canada and worldwide for her performances of the works of living Indigenous composers. In this interview, Marion discusses navigating - and stretching the limits of - the modern world of opera while proudly bearing her identity as Kwagiulth and Sto:lo. She also reflects on the complexities of balancing Indigenous traditions with modern Western music practices, her experiences working with written, recorded, and published Indigenous-composed music when, traditionally, and the role that library collections may play in her work as a faculty member at the University of Victoria. The interviewer, Kyra Folk-Farber, is honoured to be good friends with Marion as well as former singing colleagues while they both lived in Toronto, and collaborating with Marion on this interview was, unsurprisingly, a total delight.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".