Insights into an Original SSAA Choral Work of Donald Patriquin: Songs of Innocence: On Poems of William Blake
Bibliographic record
Abstract
abstract: Canadian composer, conductor, pianist, and organist Donald Patriquin (b. 1938) is\n\nbest known for his choral folksong arrangements but is also a composer of many original\n\nworks. Songs of Innocence, which Patriquin calls “one of my very best choral works,”\n\nexemplifies his approach to setting text to music and provides a rich opportunity for\n\nunderstanding Patriquin’s method of selecting text, creating a kind of libretto out of the\n\navailable text, setting the text to music, and conceiving of and composing instrumental\n\nparts equal in importance to the choral parts. Also evident in this work is his attention to\n\nsuch elements as precise word painting, varied theoretical approaches, and a general\n\nmusical aesthetic that focuses on beauty. This quintessential composition provides\n\nimportant insights into Patriquin’s personal artistry and his approach to composition.\n\nPatriquin does not fit text to music; instead, all of the musical elements are generated out\n\nof the textual nuances. Patriquin’s comments on the work and his process, gleaned from\n\nextensive email correspondence and his attendance at the U.S. premiere of the work,\n\nprovide important insights that can inform conductors and singers of his music. The study\n\nof this suite highlights Patriquin’s expert crafting of musical elements and the methodical\n\nlayering of elements he combines to tell the musical story. Pairing Patriquin’s email\n\ncorrespondence with an in-depth look at Songs of Innocence reveals his overarching\n\ncompositional ideas and underlying musical motivations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".