Bibliographic record
Abstract
Before every performance, enslaved pianist-composer Thomas “Blind Tom” Wiggins would introduce his Battle of Manassas as a programmatic depiction of the titular Confederate victory. With sharply juxtaposed fragments of Northern and Southern tunes, along with drum motifs, bugle calls, “The Star-Spangled Banner,” and vocal enactments of trains and whistles—all continually interrupted by sudden “cannon fire” in the left hand—Wiggins would finish the piece shouting “Retreat! Retreat! Retreat!” at the top of his lungs before leaving two irreverent slams of the keys to reverberate throughout the hall. Famous for his lifelong ability to perfectly imitate any music, noise, or speech he heard, Wiggins amazed his audiences for decades claiming to represent Manassas’s events exactly as he heard them described to him. But over 150 years after its premiere, scholars have tended to detect a sense of irony, if not total subversion, in the enslaved pianist’s chaotic Confederate homage. My research considers recent interpretations of the piece with an eye toward its compositional circumstances, including its supposed timeline, its printed foreword’s odd reversal of an anthem’s Southern affiliation, and its likely response to a contemporaneous battle piece by Northern pianist-composer Louis Moreau Gottschalk.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.039 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".