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Record W2101279738 · doi:10.7202/018749ar

“The folklore treasure there is astounding”

2008· article· en· W2101279738 on OpenAlexafffundvenueabout
Anna Kearney Guigné

Bibliographic record

VenueEthnologies · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsMemorial University of Newfoundland
FundersNewfoundland and Labrador
KeywordsFolkloreTreasureArt historyMOZARTNothingHistoryPublishingVisual artsArtArchaeologyLiterature

Abstract

fetched live from OpenAlex

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 collection Songs 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.262
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes4
Has abstractyes

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