The Life and Afterlife of a Folksong Collection: The Labrador Songbook Experience
Bibliographic record
Abstract
to the public. 1 It was greeted with a great deal of excitement, particularly by the composers and their relatives, who, up to that time, had only ever heard their songs sung by themselves in far-flung communities. The book launch held in Happy Val-ley became an emotional event. People were invited to come and share their experi-ences about some of the songs, and, as they told of where the song originated or how the song was sung, a few of them shed a tear for loved ones now departed. As orga-nizer of the event, I wished that I had been able to get some of this information into the book, that I had held this event in a less formal location, and that composers out-side of the Lake Melville area had been able to attend. A few songs in English, Inuktitut, and Innuamin were sung, and people went home with their copies under their arm into the cold but starry night of a Labrador winter. It was obvious that this songbook was merely a starting point, and that there were many more songs and stories to collect. What follows is a commentary on what I think has happened since the songbook became available. Editors ’ note: Tim Borlase was not only the compiler, but in many cases also the collector, transcriber of lyrics, translator, and researcher. His reflections on this publication are valu-able: this is one of the rare studies of the actual reception of a publication that has played a major role in sustaining a musical tradition; and it documents how the recovery of memories occurs through song, and how modern experiences are negotiated in relation to those memo-ries.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".