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Record W4385534573 · doi:10.1515/9780773584150

Anthems <i>and</i> Minstrel Shows

2015· book· kk· W4385534573 on OpenAlexaboutno aff
Brian Thompson

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2015
Typebook
Languagekk
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceArt

Abstract

fetched live from OpenAlex

Calixa Lavallée, the composer of “O Canada,” was the first Canadian-born musician to achieve an international reputation. While primarily remembered for the national anthem, Lavallée and his work extended well beyond Canada, and he played a multitude of roles in North American music as a composer, conductor, administrator, instrumentalist, educator, and critic. In Anthems and Minstrel Shows, Brian Thompson analyzes Lavallée’s music, letters, and published writings, as well as newspapers and music magazines of the time, to provide a detailed account of musical life in nineteenth-century North America and the relationship between music and nation. Leaving Quebec at age sixteen, Lavallée travelled widely for a decade as musical director of a minstrel troupe, and spent a year as a bandsman in the Union Army. Later, as a performer and conductor, he built a repertoire that prepared audiences for the intellectually challenging music of European composers and new music by his US contemporaries. His own music extended from national songs to comic operas, and instrumental music, as he shifted between the worlds of classical and popular music. Previously portrayed as a humble French Canadian forced into exile by ignorance and injustice, Lavallée emerges here as ambitious, radical, bohemian, and fully engaged with the musical, social, and political currents of his time. While nationalism and nation-building are central to this story, Anthems and Minstrel Shows asks to which nation – or nations – Lavallée and “O Canada” really belong.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.716
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.195
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMcGill-Queen's University Press eBooksSame topicHistorical Art and Culture StudiesFrench-language works237,207