Dancin’ in Anson: A History of the Texas Cowboys’ Christmas Ball
Bibliographic record
Abstract
In the 1880s, there wasn't much in Anson, Texas, in the way of entertainment for the area's cowhands. But Star Hotel operator M. G. Rhodes changed that when he hosted a Grand Ball the weekend before Christmas. A restless traveling salesman, rancher, and poet from New York named William Lawrence Chittenden, a guest at the Star Hotel, was so impressed with the soiree that he penned his observances in the poem Cowboys' Christmas Ball. Re-enacted annually since 1934 based on Chittenden's poem, the contemporary dances attract people from coast to coast, from Canada, and from across Europe and elsewhere. Since 1993 Grammy Award-winning musical artist Michael Martin Murphey has played at the popular event. Far more than a history of the Jones County dance, Paul Carlson analyses the long poem, defining the many people and events mentioned and explaining the Jones County landscape Chittenden lays out in his celebrated work. The book covers the evolution of cowboy poetry and places Chittenden and his poem chronologically within the ever-changing western genre. Dancin' in Anson: A History of the Texas Cowboys' Christmas Ball is a novel but refreshing look at a cowboy poet, his poem, and a joyous Christmas-time family event that traces its roots back nearly 130 years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".