Bibliographic record
Abstract
The isolated Canadian River in the Texas Panhandle stretched before John Erickson and Bill Ellzey as they began a journey through time and what the locals call “the valley.” They went on horseback, as they might have traveled it a century before. Everywhere they went they talked, worked, and swapped stories with the people of the valley, piecing together a picture of what life has been like there for a hundred years. Through Time and the Valley is their story of the river—its history, its lore, its colorful characters, the comedies and tragedies that valley people have spun yarns about for generations. Outlaws, frontier wives, Indian warriors, cowboys, craftsmen, dance-hall girls, moonshiners, inventors, ranchers—all are part of the Canadian River country heritage that gives this book its vitality. “Through Time and the Valley is the finest non-scholarly account of the history, culture, and people of this region. . . . What I did notice was humor, pathos, strong characterization, crisp dialogue, and such a sense of place as to bring a lump to my throat.” — Roundup Magazine
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".