“A Knowing So Deep It's Like a Secret”: Recent Approaches to Race, Identity, and Transformation in Toni Morrison's Fiction
Bibliographic record
Abstract
Abstract: In the burgeoning field of Toni Morrison studies, three scholars have recently made original contributions to the field with their interdisciplinary scholarship, showing a trend in Morrison studies of forging new readings through the use of comparative and cross-cultural techniques. In so doing, Evelyn Jaffe Schreiber, K. Zauditu-Selassie, and Rebecca Hope Ferguson help to elucidate Morrison's complex relationship to the past and to African and American culture as well as the consistent ethical concerns across her body of work. Zauditu-Selassie focuses primarily on Morrison's references to African cosmology, while Ferguson and Schreiber take interdisciplinary approaches to key themes (transition, exchange, and the concept of home) in Morrison's work. While Ferguson and Schreiber tend to problematize Morrison's relationship to an African past, Zauditu-Selassie takes a much more celebratory approach regarding her use of cultural and spiritual references.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".