Breaking Down Walls in Post-Pandemic Transnational Fables: Kazuo Ishiguro’s <i>Klara and the Sun</i> and Mohsin Hamid’s <i>The Last White Man</i>
Bibliographic record
Abstract
Kazuo Ishiguro’s Klara and the Sun and Mohsin Hamid’s The Last White Man are fables that indirectly engage with the COVID-19 pandemic. While neither novel explicitly mentions the pandemic, they offer subtle reflections on this period in our history and forecast COVID’s fallout. Ishiguro uses a childlike persona to explore a dystopian world involving artificial intelligence and gene editing. Hamid elliptically addresses the pandemic in the context of a resurgence of racism and the resilient response from Black Lives Matter activists over the past half-decade. I examine the two authors’ indirect representations of the pandemic and the way in which it triggers reflections on those partitions and fences put up to divide us. The pandemic’s inherently challenging nature for direct representation is echoed in these writers’ decision to create fables not tied to any particular country. While Ishiguro’s and Hamid’s novels evoke ideas about disease, death, and bereavement, they primarily focus on loneliness, digital dependency, and the social divisions that have arisen, especially since 2016 due to events such as Brexit and the election of Donald Trump. Readers look through Ishiguro’s titular cyborg character Klara’s eyes as she tries to make sense of the boxes, rectangles, partitions, and walls that she sees. And Hamid uses absurdism to explore racial classification, whereby characters lose their whiteness and cross over to an unstated ethnicity for the rest of their lives. These authors’ transnational narratives encourage breaking down the metaphorical and physical barriers that have divided people. Through their fable-like storytelling, the novelists strive to connect people and blur the lines that separate us.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".