9 Canadian Folk Music 47.2 (Summer 2013) “Waiting for a Train ” in Canada
Bibliographic record
Abstract
It was a good jam despite my guitar being outnum-bered two to one by banjos. At one point I sang Jim-mie Rodgers’s “Waiting for a Train ” (a.k.a. “All Around the Water Tank”). I’d known this song for years, but had recently dusted it off after hearing Roy Forbes’s fine version.1 Waiting for a Train (Jimmie Rodgers’s version) All around the water tank, waiting for a train, A thousand miles away from home, sleeping in the rain; I walked up to a freight man to give him a line of talk, He said, “If you’ve got money, I’ll see that you don’t walk”; “I haven’t got a nickel, not a penny can I show”, “Get off, get off, you railroad bum, ” and he slammed the boxcar door. Well, they put me off in Texas, a state I dearly love, Wide open spaces around me, moon and stars above; Nobody seems to want me or lend me a helping hand, I’m on my way from Frisco, going back to Dixieland; My pocket book is empty and my heart is full of pain, I’m a thousand miles away from home, waiting for a train. I was intrigued when John Leeder said he had heard his father sing it in 3/4 time. All of the recorded ver-sions I’d heard were in 4/4 time (e.g., Jimmie Rodg-
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.304 | 0.068 |
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".