Deconstructing Betrayal, Discrimination and Guilt in Khaled Hosseini’s “The Kite Runner”
Bibliographic record
Abstract
The current study undertakes a detailed analysis of Khaled Hosseini’s The Kite Runner representative novel. Hosseini, an Afghan born American writer depicts a war-torn Afghanistan in various universal themes i.e., family re-union, discrimination, regret, childhood, guilt, womanhood, betrayal, religion and salvation that played a considerable role in abating commission of crimes in Afghanistan during pre and post-Taliban periods which ended up shaping the interminable psychological scars of the protagonist. In his work, Hosseini reveals the devastating status of Afghans in general and women and children in particular who have, for decades, been irrationally marginalized and confined to the four walls of their homes by the society. His quests for wealth, love, loyalty and unqualified peace among Afghan citizenry whom he equates to have rights just like other human beings globally is the only means through which the protagonist considers a key to chart out a new future. Moreover, in reference to the Pashtun and Hazara ethnic communities’ customs and traditions and by use of historical, factual, real and fictional information, the article discusses the constructive human relations in a society bedeviled with mistrust, confusion, doubt and betrayal. Besides, by adopting the historical perspective method, the study examines how discrimination as a theme has been utilized to portray Hosseini’s literary image as a protagonist writer. Finally, a summary of the paper along with recommendations is made in the conclusion section.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".