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-
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.047 | 0.014 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".