American Agony: Richard Wright’s Language of Pain in <i>Black Boy (American Hunger)</i>
Bibliographic record
Abstract
This article examines Richard Wright’s description of bodily pain in his autobiographical book Black Boy (American Hunger): A Record of Childhood and Youth (1945). In the last few decades, there has been an ongoing debate about both the nature of physical pain and, in turn, its relationship to language. After the publication of Elaine Scarry’s monumental The Body in Pain (1987), in which the argument is made that physical suffering is “language-destroying,” there has been a burgeoning of research that has challenged this claim by tracing the dynamic relationship between the experience of pain and the social institutions, cultures, and languages that influence our perceptions of it. This article analyses the way in which Richard Wright’s text situates the first-hand qualia of hunger-pains (along with other forms of somatic anguish) within the broader social setting of anti-Black racism and violence during America’s Jim Crow era to, in turn, offer a representation of bodily agony that entwines it with the psycholinguistic dimensions of consciousness. Instead of depicting physical pain in terms of what Patrick Wall calls “a rigid, simple signaling system,” Wright collapses the dichotomy between psychological torment and corporeal distress to offer a nuanced portrayal of pain that entwines its moment-to-moment sensations with the psychological process of navigating America’s cultural matrix of anti-Black dehumanization, poverty, and hostility. As a result, Black Boy serves as a valuable example of the way in which creative storytelling can represent the culturally codified structures of feeling that shape even the most private forms of bodily hurt.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".