Unravelling Dystopia in Dreams of Resurrection: Searching for Meaning Amid Chaos
Bibliographic record
Abstract
Amidst the global COVID-19 epidemic, there has been a growing surge in the popularity of dystopian literature, notably in the Arab world. This analysis delves into a unique piece of this literature, Ahlam Al Qeyamah (2018) by Egyptian writer Mohammad Gamal, translated as Dreams of Resurrection and released in 2021. This paper not only explores the dystopian characteristics included in the novel and their connection to the pandemic but also highlights the unique narrative style and thematic elements that set this work apart. The novel, which ignited substantial controversy in the Arab world, portrays a virus spreading in Egypt via tourists at an international airport. Gamal's narrative delves into the intricacies of a dystopian society, gradually unveiling its depths as the novel progresses. This paper presents the characteristics of classic dystopian writing as a structure for examination and offers a concise summary of the genre's evolution in Arabic literature. The discourse emphasizes shared components and discerns specific themes in Dreams of Resurrection. The results suggest that the work conforms to Erika Gottlieb's defining characteristics of dystopian literature, clearly placing it within the category. Furthermore, this interpretation emphasizes that dystopian literature does not support senseless suffering but instead aims to discover significance in a world devoid of meaning and to provide purpose among disorder.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".