Bibliographic record
Abstract
In this thesis, I propose a study of Toni Morrison's novels with attention to her fictional representations of African-American communities. I demonstrate how she contests constructions of a homogeneous American communal identity through representations of diverse African-American experiences. Further, I examine the processes of communal identification depicted in her work. Some literary critics have analyzed Morrison's representations of community as either a hindrance or a help in the development of individual characters. But, because Morrison's communities are situated in different regions and decades, and formed under different circumstances, my study of her novels involves an exploration into how, why, and where these communities are formed with attention to space, place, history, and function. I argue that the communal spaces, places, and identities in the novels The Bluest Eye, Sula, and Paradise are constructed by, and out of, social interactions. I also demonstrate how communal identity is a process, not a product, and how it is consistently and continually subject to the forces of history, culture, and power. This particular perspective on identity demands an acknowledgment of the past and how it informs the present, but it also demands a recognition of the ways in which communities are constructed relationally. I also explore the significance of history, memory, and storytelling in Jazz, Tar Baby, and Beloved. I point to how these elements are vital to processes of healing and how they are important to the survival of her varied communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".