Bibliographic record
Abstract
Lionel Daunais was an eminent and beloved 20th-century Québécois musician who contributed greatly to the performing arts in Canada. Through his work with the Trio Lyrique, Les Variétés Lyriques, and his numerous compositions, he wielded a potent sphere of influence on the Canadian musical landscape. Lionel Daunais's compositions constitute a significant oeuvre, comprising solo vocal works, song cycles, folksong arrangements, individual choral works, and multi-movement choral works. Marked by irresistible wit, the melodicism of French mélodie, and the absolute eminence of the text, Figures de danse is his most well-known multi-movement choral work. Daunais penned the earliest extant version of Figures de danse in 1947, however, the work emerged into Québec's choral scene in the mid-1970s via the establishment of the Alliance des Chorales du Québec. This set of tragicomic caricatures, which sets beautiful choral and piano writing to clever—and sometimes hilariously nonsensical—texts by Daunais himself, is accessible for performance by youth choirs, community choirs, university choirs, and professional choirs alike. Unfortunately, various factors (e.g. the separation of the choral and piano scores, local references, and score errors) often stymie its performance. The purpose of this dissertation is to ameliorate these challenges via a conductor's guide and to advocate for the performance of Daunais's chef-d'oeuvre.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.059 |
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".