A Generation of Witnesses: Neo-Testimonial Practices in Flight to Canada, Dessa Rose, Beloved, Kindred, and the Chaneysville Incident
Bibliographic record
Abstract
Scholar Henry Louis Gates, Jr. observes that "fact and fiction have always exerted a reciprocal effect on each other" ("Authenticity" 29). Authors of neo-slave narratives – postmodern renderings of the slave experience – illustrate this reciprocation as they engage in the inventive (re)telling of historical events from the privileged vantage of the present. This study examines the role imagination plays in reconstructing a marginalized, forgotten past. Additionally, this study discerns the neo-testimonial patterns – the narrative techniques inspired by the languages, experiences, and memories of the African diaspora – that the neo-slave narrative authors employ as they merge history with imagination in the creation of a fictionalized history. Although critics have already noted the existing relationship between history and fiction in these narratives, how authors finesse the line between history and imagination deserves closer examination. This study looks carefully primarily at Ishmael Reed's Flight to Canada, Sherley Anne Williams' Dessa Rose, Toni Morrison's Beloved, Octavia Butler's Kindred, and David Bradley's The Chaneysville Incident. By examining the dynamics of the commingling of history and imagination in these narratives, this study contributes to an understanding of the role of rememory and/or embellishment in the neo-slave narrative (sub)genre.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".