Bibliographic record
Abstract
Few published collections of Gaelic song place the songs or their singers and communities in context. Brìgh an Òrain - A Story in Every Song corrects this, showing how the inherited art of a fourth-generation Canadian Gael fits within biographical, social, and historical contexts. It is the first major study of its kind to be undertaken for a Scottish Gaelic singer. The forty-eight songs and nine folktales in the collection are transcribed from field recordings and presented as the singer performed them, with an English translation provided. All the songs are accompanied by musical transcriptions. The book also includes a brief autobiography in Lauchie MacLellan's entertaining narrative style. John Shaw has added extensive notes and references, as well as photos and maps. In an era of growing appreciation of Celtic cultures, Brìgh an Òrain - A Story in Every Song makes an important Gaelic tradition available to the general reader. The materials also serve as a unique, adaptable resource for those with more specialized research or teaching interests in ethnology/folklore, Canadian studies, Gaelic language, ethnomusicology, Celtic studies, anthropology, and social history.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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.
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".