The Role of Superstition in Twain’s The Adventures of Huckleberry Finn and Shakespeare’s Macbeth: A Comparative Study
Bibliographic record
Abstract
This article is an attempt to explore the inclusion and the use of superstitious elements in Mark Twain’s novel The Adventures of Huckleberry Finn (1884) and Shakespeare’s play Macbeth (1611). Superstition involves a deep belief in the magic and the occult, to almost to an extent of obsession, which is contrary to realism. Through the analytical and psychological approaches, this paper tries to shed light on Twain’s and Shakespeare’s use of supernaturalism in their respective stories, and the extent the main characters are influenced by it. A glance at both stories reveals that characters are highly affected by superstitions, more than they are influenced by their religious beliefs, or other social factors and values. The researcher also tries to explore the role played by superstition, represented by fate and the supernatural in determining the course of actions characters undertake in both dramas. The paper concluded that the people who lived in the past were superstitious to an extent of letting magic, omens; signs, etc. affect and determine their lives; actions and future decisions. They determine their destiny and make it very difficult for them to avoid it, alter it or think rationally and independently. And that, man’s actions are not isolated, but closely connected to the various forces operating in the universe.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".