Bibliographic record
Abstract
Feeling Subjects: Science and Law in Nineteenth-Century America challenges cultural assumptions about feeling and politics articulated by Uncle Tom's Cabin's demand "to feel right" in opposition to the purportedly dispassionate disciplines of American science and law. By juxtaposing literature by African American and Asian American authors alongside works by white novelists of the American Renaissance and popular white women writers, this project analyzes literary portrayals of individual and disciplinary subject formation in relation to scientific and legal discourses in the culture of sentiment. The "Affectations" chapters argue that the practitioners of science and law use the language of sentimentality to reconfigure the limits of sympathy. I pair chapters on Herman Melville's Benito Cereno and Martin Delany's Blake on the role of blackness. In Melville, I trace how Captain Delano's benevolent racism toward enslaved black subjects is achieved through the sentimental logic that undergirds race science and the Fugitive Slave Act of 1850. In contrast, Delany, as the founder of Black Nationalism, imagines a new form of sympathetic kinship between African Americans and Native Americans in order to reclaim science and law as part of a liberatory system of feeling that can unite black, indigenous, and Asian subjects in rebellion. Conversely, the "Disaffections" chapters examine how gendered and racialized forms of unfeeling resist normative oppressions naturalized through feeling. I examine women doctors who manipulated the unfeeling professionalism of medicine in order to divert their emotional lives away from heteronormative imperatives. Sarah Orne Jewett's and Elizabeth Stuart Phelps's novels dramatize this dynamic: the queerly frigid woman doctor clashes against her male antagonist/love interest, a lawyer who embodies the naturalized patriarchal order. My final chapter traces the trope of Oriental inscrutability in the Yellow Peril discourse articulated in race science and political speeches that led to the Chinese Exclusion Act of 1882. In an examination of Sui Sin Far's short stories, I argue that Oriental inscrutability is a tactic for Chinese women to evade the epistemological mastery of whiteness. The project intervenes in the antisocial turn in queer and affect theory by proposing that unfeeling can be a survival tactic for marginalized subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".