“Befo’ de Wah”: Sounding Out Ill-Legibility in Charles W. Chesnutt’s Conjure Stories
Bibliographic record
Abstract
In 1969, blues guitarist Earl Hooker released Two Bugs and a Roach, solidifying him as a pioneer of the wah-wah technique. Before the wah-wah pedal, however, there was Charles W. Chesnutt’s Conjure Stories, a collection of frame narratives that recollect plantation life “befo’ de wah”. In this essay, I insist the slide, slip, and compressions of Hooker’s wah-wah voicings find resonance in Chesnutt’s own linguistic play, through which the sonics of Julius’ sociolect texture the text towards speculative spellings, grammars, and meanings that query the logics of white, Enlightenment rationality and its hegemonic conceptions of space, time, value, and subjecthood. In listening to the tales’ resonances with the “wah”, I suggest Chesnutt articulates the “ill-legibility” of plantation existence and its echoes into and out from the present, as evidenced by Hooker’s own disproportionate susceptibility to and lifelong struggle with tuberculosis. In doing so, Julius’ storytelling makes legible modes of survival that attune to how Black bodies persist via the (un)sound logics of illness, slavery, and sonority. Overall, I argue Chesnutt amplifies modes of existence that emerge from the distinct spatio-temporality of the plantation, thus re-forming with and through the ills of slavery and persisting against rational legibility, capital production, and normativity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".