Bibliographic record
Abstract
Injustice, oppression, and bad treatment are among those habits that have been lingering in societies that many writers have been trying hard to address throughout the ages. Among those writers is Langston Hughes in his story “Cora Unashamed” where he has boldly presented the real struggle of black people with a white system.“Cora Unashamed” appeared in his work The Ways of White Folks (ed. 1971). The important relationship in this story is that of the white and black races; in the story, Cora represents the blacks while the Studevants and their daughter are white. Worth mentioning is the male-female relationship in this story. By exploring the concept of racial master-slave relationship and studying the idea of the whites’ superiority, the paper aims at scrutinizing how the whites use racism and slavery to approve their domination over the blacks. Besides, it investigates the impacts of slavery and racism on the blacks. Close-reading method is employed to analyze the story. Besides, the contextual method which focuses on intrinsic and extrinsic elements is utilized. The result of the analysis shows that race helps in the whites’ domination over other minorities, particularly the blacks. In other respect, master-slave relationships have their great negative economic impacts on the poor people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".