Bibliographic record
Abstract
In 1949, anthropologist Marius Barbeau recruited Margaret Sargent, a young classically trained musician from Ontario to work for him at the National Museum of Canada. As the first ever musicologist to be employed by this institution, Sargent’s first task was to transfer Barbeau’s wax cylinder sound recordings to magnetic tape. While working on Barbeau’s massive collection, Sargent became interested in collecting folksongs and proposed to him the idea of going to Newfoundland to do research. With Barbeau’s support, in 1950 she spent eight weeks in the province, collecting folksongs, fiddle tunes, and other folklore materials mainly in St. John’s and Branch. Despite launching the first Canadian funded research into Newfoundland’s folksong traditions, little is known about Sargent’s activities for the National Museum mainly because she published nothing of her Newfoundland work. Instead, her successor Kenneth Peacock is often viewed as launching this research. Although Peacock later visited the province six times, eventually publishing a three-volume collectionSongs of the Newfoundland Outports(1965), it was Sargent who initiated the Museum’s folksong research program in that province. This essay, which is based in part on interviews with Sargent, as well as her field notes and tapes, provides a detailed account of her Newfoundland fieldwork and of the kinds of material she was able to acquire during her one summer of fieldwork. It highlights the fieldwork challenges Sargent faced while in Newfoundland and how her groundbreaking fieldwork paved the way for Peacock’s later research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".