Bibliographic record
Abstract
Recognizing the broad potential of singing as a facilitator of moral instruction, academic learning, and societal participation, New Hampshire native Asa Fitz (1810–1878) was committed to advancing music and music education. A prolific publisher, editor, and author, he was involved in the production of dozens of works filled with songs and music, designed to further everything from reform movements to congregational singing, spiritualism to family life. As a singing master and music teacher, he instructed both children and other teachers, promoting his song books, his instructional techniques, his personal principles, and his overriding belief that everyone could, and should, learn to sing. Yet, for all that he was well known during his lifetime, little scholarly attention has been paid to the man, his philosophical underpinnings, or his disparate publications. This article focuses on the development of his “new system of figured music,” culminating with the publication of School Songs for the Million! in 1850. It briefly reviews the concept and expressions of alternate systems of musical notation in early to mid-nineteenth-century America and then places Fitz within that context, as he created, developed, and promoted his system to children and teachers. Though School Songs for the Million! was not as commercially successful as some of his other titles, it serves to demonstrate Fitz’s willingness to experiment with unconventional and controversial ideas in an effort to advance participation in music.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".