<scp>Erin Forbes</scp>. <i>Criminal Genius in African American and US Literature, 1793-1845</i>
Bibliographic record
Abstract
The title of this book may mislead: in the popular imagination, the phrase ‘criminal genius’ connotes nearly the opposite of what it means in Erin Forbes’s account of early African American literature. Rather than ‘the individualism of a lone male figure malevolently standing against society’ (p. 18)—Professor Moriarty, for instance, or Dr Hannibal Lecter—criminal genius here ‘designates racialized figures that exceed abstract notions of the liberal, isolated subject; cut against colonial gender hierarchies; and work instead more broadly through assemblages of print culture, human being, and the material world’ (p. 25). In Forbes’s framing, both crime and genius are fundamentally collective: the former because it is defined by and implicates society at large, and the latter because its earlier meaning signifies ‘a unity, or collective spirit’ (p. 162) rather than an exceptional individual. As signalled by the word ‘assemblages’, Forbes’s criminal genius is for better and worse swathed in the discourses of New Materialism and Actor-Network Theory, foregrounding the agency of ‘a larger collective of human, material, and environmental forces’ (p. 17) while also encompassing the ‘voluntarist’ (p. 18) human agency that such frameworks usually exclude.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".