Bibliographic record
Abstract
Crossing Over Fiddle and Dance Studies from around the North Atlantic 3Ken Perlman is a professional musician who conducts independent research related to the fields of folklore and ethnomusicology.He has spent two decades collecting tunes and oral histories from traditional fiddle players on Prince Edward Island, Canada.He has produced three anthologies devoted to field recordings of traditional PEI fiddlers, and published a collection of over 400 tunes called The Fiddle Music of Prince Edward Island.He is now working with the Canadian Museum of Civilization on the production of a website which will be focused on traditional PEI fiddling.228 18 KEN PERLMAN D uring the heyday of community dance-fiddling, elaborate stereotypes depicting fiddlers as lazy, drunken 'ne'er do wells' grew up in many Celtic and North American fiddling cultures.And yet, according both to first-hand accounts and the secondary literature, these same fiddlers provided a service that was essential to the social and material lives of their communities.Using Prince Edward Island (PEI) in eastern Canada as a case-study, I will explore the contradiction between these two disparate images. 1 Prior to the 1960s and 1970s, when twentieth-century technology and social organization became established in rural PEI, people had a pretty clear set of ideas concerning the fiddler's role in the community, or district.Dances were the most common expression of district social life.And whenever there was a dance in the offing, it was the fiddler's duty to make himself available to play.The most common community dance was the house party, as described by Neil MacCannell of Lorne Valley:
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".