Songs of the North Woods as sung by O.J. Abbott and collected by Edith Fowke
Bibliographic record
Abstract
Edith Fowke (1913-1996) was a renowned Canadian folklorist, folk song collector, researcher, writer, and teacher who during her long career recorded nearly two thousand songs. Awarded the Order of Canada in 1978 and named a Fellow of the Royal Society of Canada in 1983, Fowke's legacy is recognized by folk singers and scholars alike as the most comprehensive work in its field. Producing radio programs for the CBC throughout the 1950s and 1960s, she was responsible for discovering such eminent singers as LaRena Clark, Tom Brandon, and O. J. Abbott. O. J. Abbott was one of Fowke's most prolific singers, as she collected and recorded over 120 of his songs, 66 of them transcribed for this collection. The songs, mostly of Irish origin, were popular among settlers to the Ottawa valley and in the lumber camps of northern Ontario in the late 1800s. Born in England in 1872, Abbott worked throughout Ontario and Quebec in lumber camps before settling in Hull, Quebec. He recorded numerous records for the Folkways label and performed with such folk heroes as The Travellers, Ian and Sylvia, and Pete Seeger. Songs of the North Woods as sung by O.J. Abbott and collected by Edith Fowke includes a detailed musical analysis that outlines the meter, scale, and range of each song, an index that indicates where each song can be found on the original source tapes, and extensive field notes, interviews, and recording details.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".