Decoding Pacifism in Contemporary Diaspora Literature: The Anti-War Message in the Novels of Khaled Hosseini and Tahmima Anam
Bibliographic record
Abstract
In a world where war and destruction are passions, it is truly important to realize that violation is not the ultimate solution to any problem. The true answer lies in resorting to the understanding of Pacifism. Inspired by Jane Addams as a key proponent in criticizing the war, the researchers use Daniel Diederich Farmer’s approach to show how the theory of Pacifism posits that human interactions should be governed by peaceful means rather than resorting to violence or aggression. It advocates the use of arbitration, surrender or migration as methods to resolve wars. In this paper, the researchers use the qualitative method by comparing the novels of two diasporic authors, Khaled Hosseini and Tahmima Anam, which focus on war's consequences on individuals who are displaced from their homeland. The researchers also compare authors' portrayal of the relationship between war and diaspora in their literary works. Additionally, the article emphasizes the importance of the Pacifism theory by examining examples of marginalized characters in Hosseini and Anam's novels who experience displacement. The result of the study reveals that the theory of Pacifism, which is present in the novels of the select authors, underscores the value of seeking peace and migration as practical solutions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".