The Experience of Alienation in Season of Migration to the North and Song of Solomon : A comparative Study of Mustafa Saeed and Milkman
Bibliographic record
Abstract
The study aims at tracing out the comparable value Al-tayeb Saleh and Toni Morrison partake and deciphers it through their narratives Season of Migration to the North (SMN) and Song of Solomon (SS). It investigates Afro-Arabs and Afro-Americans’ sense of alienation, a recurring theme in modern literature in general and the novel in particular. These novelists present a painstaking study of the effects of alienation which is a result of loss of identity on Afro-Arabs and Afro-Americans represented by Mustafa Saeed and Milkman. It holds out how the manifestation of alienation in both narratives are exhibited and also to show to what extent time and place have a role in promoting alienation features. A major objective of the study is to show how racism and the white values affect Afro-Arabs and Afro-Americans who undergo the impact of colonialization and materialism for ages. The comparative methodology is used to show the outcome of adopting the white values and neglecting the past. It is concluded that denying one’s history and adopting the destructive ideas of the other that contradict societies that have an intellectual, cultural and religious legacy is ruinous as it leads one to alienation. Getting the best of the other as well as preserving original roots is necessary for a healthy and balanced personality. What impels me to tackle such topic is the timeless trauma and dilemma shared and experienced by Afro-Arabs and Afro-Americans due to their color.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".