Bibliographic record
Abstract
Bad Company and Burnt Powder is a collection of twelve stories of when things turned "Western" in the nineteenth-century Southwest. Each chapter deals with a different character or episode in the Wild West involving various lawmen, Texas Rangers, outlaws, feudists, vigilantes, lawyers, and judges. Covered herein are the stories of Cal Aten, John Hittson, the Millican boys, Gid Taylor and Jim and Tom Murphy, Alf Rushing, Bob Meldrum and Noah Wilkerson, P. C. Baird, Gus Chenowth, Jim Dunaway, John Kinney, Elbert Hanks and Boyd White, and Eddie Aten. Within these pages the reader will meet a nineteen-year-old Texas Ranger figuratively dying to shoot his gun. He does get to shoot at people, but soon realizes what he thought was a bargain exacted a steep price. Another tale is of an old-school cowman who shut down illicit traffic in stolen livestock that had existed for years on the Llano Estacado. He was tough, salty, and had no quarter for cow-thieves or sympathy for any mealy-mouthed politicians. He cleaned house, maybe not too nicely, but unarguably successful he was. Then there is the tale of an accomplished and unbeaten fugitive, well known and identified for murder of a Texas peace officer. But the Texas Rangers couldn't find him. County sheriffs wouldn't hold him. Slipping away from bounty hunters, he hit Owlhoot Trail.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.686 | 0.454 |
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".