Fiddling with Technology: The Effect of Media on Newfoundland Traditional Musicians
Bibliographic record
Abstract
THE DEVELOPMENT OF SOUND technologies since the late nineteenth century has been as revolutionary for musicians as the printing press was for verbal communi-cation in the fifteenth century. By the late twentieth century, the remotest of vil-lages had access to new sounds and information from across the globe. Not unexpectedly, these processes have had a major impact on folk music traditions around the world, including those in Newfoundland and Labrador. This article will examine how media sources, such as radio, recordings, television, and printed mu-sic have transmitted new styles of music to the island and how these, in turn, have influenced the repertoire and styles of traditional fiddle players. I will focus mainly on musicians who began learning in a community context, around or before mid-century, and consider how they used multiple technologies to access and learn new styles. I will draw primarily on field research conducted between 2000 and 2002 throughout the island of Newfoundland, with a particular focus on musicians from Bay de Verde, Conception Bay. All of these fiddlers have learned local tunes from the aural tradition, and media sources have supplemented this repertoire. 2 As Herbert Halpert noted in his preface to Taft’s discography of Newfoundland music: One point should be emphasized. A vigorous folk culture is not overwhelmed by modern commercial music. It does not feel impelled to absorb all the new musical idi-oms to which it is exposed. Apparently a strong culture is able to adopt selectively from many styles only those elements that it wants, those that it can adjust to its own way of feeling. (v)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".