Opera Indigene: Re/presenting First Nations and Indigenous Cultures. Ed. by Pamela Karantonis and Dylan Robinson.
Bibliographic record
Abstract
The pairing of opera and indigeneity in the title of Pamela Karantonis and Dylan Robinson’s terrific edited collection is intriguing, but not intuitive. Opera is widely assumed to be European or Western, an identification that typically excludes Indigenous persons and groups. Likewise, Indigenous musics are stereotypically limited to ‘traditional’ forms, which excludes opera. Such cultural fundamentalism, though common, is hardly defensible in an age of pervasive creative globalization and mixture, including in contemporary Native artistic scenes. A signal contribution of Opera Indigene: Re/presenting First Nations and Indigenous Cultures is its refusal of this fundamentalism through its documentation of Indigenous participation in opera and musical theatre, past and present. The volume also makes an important contribution to the existing musicological scholarship on the treatment of Native or Indigenous topics in opera, especially by non-Indigenous artists. In sum, it is an original, worthy addition to the scholarly literature on opera and musical theatre, Indigenous musics, and cultural and performance studies. With its lively and approachable discussion of a wide range of operas it is sure to appeal to general readers as well.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".