Bibliographic record
Abstract
Before Newfoundland: Maud Karpeles in CanadaMaud Karpeles is best lmown for her folksong collecting with Cecil Sharp in the Southern Appalachians, her biography of Sharp, and her work for the International Folk Music Council.During 1929 and 1930 she made two collecting trips to Newfoundland, and eventually published most of what she gathered there in the 1971 edition of Folk Songs from Newfoundland.Although this was Karpeles' most important solo work as collector, during the late 1920s she also noted songs and dance tunes in the U.K., Canada, and New England.This aspect of Karpeles' work seems to have been completely ignored.The aim of this paper is to shed some light on Karpeles' activities as a tune-hunter between the death of Cecil Sharp and her second trip to Newfoundland.The main focus will be on Karpeles' collecting in Ontario, Saskatchewan, and New England.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.044 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".