"Aristotle’s Logic of Syllogism and Mimesis as Applied to Speculating and Imitating Pandemics in St. John Mandel’s Station Eleven (2014) and Amir Tag Elsir’s Ebola 76 (2012)"
Bibliographic record
Abstract
This paper examines the applicability of the Aristotelian logic of syllogism and mimesis to speculating and imitating epidemics through two pandemic narratives. Two literary texts relating to two different cultures are analyzed: Ebola 76 (2012) by the Eastern Sudanese Amir Tag Elsir which is considered a prescient of the second wave of Ebola in 2014 and Station Eleven (2014) by the Western Canadian St. John Mandel which is also considered a prescient of COVID-19 in 2020. These viruses are logically speculated and imitated in fiction because of the certain mutation of previous similar viruses. Following Aristotle’s logic, these two novelists apply the deduction method of reasoning to their texts. Aristotle’s logic claims that any certain conclusion should start first with two premises. The first premise is that there is a virus. The second premise is that this virus mutates. Then, the certain conclusion is a newly mutated virus. Thus, despite cultural differences, both novelists were able to imitate the past and the present just to speculate about future pandemics in real life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".