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Record W1565068675

From Acetate Disc to Digital Audio: Tracing the Copies of Helen Creighton’s Sound Recordings

2009· article· en· W1565068675 on OpenAlexaboutno aff
Creighton Barrett

Bibliographic record

VenueCanadian Folk Music / Musique folklorique canadienne · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsFolkloreNova scotiaMusicalFolkloristicsArt historyArtPhonographChoirHistoryVisual artsLiteraturePhysicsArchaeologyAcoustics
DOInot available

Abstract

fetched live from OpenAlex

In 1928 Helen Creighton started collecting old songs in towns and villages around Halifax, Nova Scotia. She had little musical training and no recording equipment so she began by transcribing tunes with a melodeon, a hand organ that was cranked with one hand and played with the other. Through much repetition with her singers, she collected and transcribed enough tunes to publish Songs and Ballads from Nova Scotia (Creighton, 1975). Creighton briefly experimented making recordings on wax cylinders, but she would not find a consistent way to make recordings until the 1940s. When Helen Creighton attended the Institute of Folklore in 1942, she met Alan Lomax, who asked if she would consider using a portable disc recorder in Nova Scotia (Creighton, 1975). Lomax’s offer was timely. Shortly before the Folklore Institute, Helen had what she called an “unfortunate encounter” with Laura Boulton, a folklorist who collected songs throughout Canada and the United States in 1941 and 1942 for the National Film Board of Canada. With some prodding from Marius Barbeau, Helen reluctantly took Boulton to see many of the same singers she had previously made transcriptions from (McMillan, 1991, p. 71). Helen later wrote that Boulton had “come to my province and had attempted to take for herself on disc all the songs I had collected so laboriously” and that Boulton insinuated “that only someone with her massive brain could operate a machine so intricate” (Creighton, 1975, p. 131). With this in mind, Creighton happily accepted the offer from Lomax. After the Folklore Institute, she went back to Nova Scotia through Washington D.C. so she could learn how to use the machine at the Library of Congress (Creighton, 1975, p. 132-133). The equipment arrived in seven boxes in July 1943. The Library of Congress disc recorder was used for two collecting projects during the 1940s, once in 1943 and 1944, and again in 1948. By this time Creighton was recording on audio reel for the Canadian Museum of Civilization (CMC). In total she collected over 4,000 songs and deposited her recordings at the Library of Congress, NSARM, CMC, and Mount Allison University. It is a massive and far-reaching collection of national significance. The recordings from 1943 and 1944 are some of the most important recordings Helen made. The war-time Library of Congress project was justified by having Creighton record navy songs and performances, but the bulk of the material she recorded was traditional songs and stories in English, French, German, Mi'kmaq, and Gaelic. With the disc recorder, Creighton was able to go back to her singers and record songs she originally transcribed with the melodeon. After checking the tunes transcribed with the melodeon with the tunes recorded on disc, eleven of the songs published in the first edition of Songs and Ballads from Nova Scotia were revised for the 1966 reprint (Creighton, 1972). Many of the songs were also published in other books. There are multiple recordings of certain songs, either by different singers or by the same singer, so the recordings contain fascinating examples of how traditional songs evolve, even over short periods of time. The recordings from 1943 and 1944 are among Creighton’s most significant, and for various reasons, they are among the most heavily preserved by the archival institutions holding the recordings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.007

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 head0.030
GPT teacher head0.200
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2009
Admission routes1
Has abstractyes

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