Bibliographic record
Abstract
This paper deals with the life and work of Ignatius Rumboldt, a formidable force in the development of the choral art in Newfoundland and Labrador. Data has been gathered through the examination of journal and newspaper articles, and a series of personal interviews, including comments made by Dr. Rumboldt himself. I examine his involvement in the establishment of many community choirs across Newfoundland, a choral network that spanned the entire province, from Labrador to the Avalon Peninsula. Most importantly, the paper focuses on the phenomenal success that Dr.Rumboldt enjoyed while sowing the seeds of The Joy of Singing wherever he went. It attempts to supply an answer to the question Why did everyone love to sing for Nish? and while doing so, it will demonstrate that much of the popularity of choral singing and the singing culture in general in this province can be directly linked to his labours of love. In addition to articles and interviews, I have examined materials found in a collection of Dr.Rumboldt's electronic tapes and personal papers which has been recently put into the MUN Folklore Archive. What better place could there be for this paper on the life and work of Nish Rumboldt, than right here and now, at a conference based on the phenomenon of singing? After all, it was this very phenomenon that enabled Nish to work his magic making him Newfoundland's beloved Pied-Piper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".