Struggle and Survival in Cultural Clash: A Case Study of Pecola in The Bluest Eye
Bibliographic record
Abstract
Toni Morrison is not only one of the Afro-American writers who focus on the clash between black culture and the white mainstream culture in the United States as well as the marginalized existence of the blacks, but more importantly a unique Afro-American woman writer who goes beyond the simplistic dichotomies of the black male literary tradition and explores the root of the tragedy of the blacks in the mainstream society. Based on textual analysis of her first novel The Bluest Eye and a case study of Pecola, a main character in this novel and actually a victim and scapegoat, this paper, with the painful truth that Pecola’s tragedy results not just from the denial and rejection of the mainstream society, but more significantly, from the blind identification of some blacks in the mainstream culture, and their incompetence to cherish their own culture and identity, aims at exploring hope in the tragic story, and suggesting how blacks can struggle to survive so as to extend their heritage and values.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".