Human Voices: Language and Conscience in Twain's <i>A Connecticut Yankee in King Arthur's Court</i>
Bibliographic record
Abstract
Many critics writing on Mark Twain's A Connecticut Yankee claim that the novel eludes easy interpretation because of its complex ironic twists, its juxtaposition of comedy and tragedy, and its penchant for pointing the sword of satire both at the pre-industrial Arthurian world and at Hank's own industrialized America. This confusion has led some critics to throw up their hands and write off the novel as one of Twain's artistic “failures.” However, exploring the novel's use of language and the role of story-telling, in particular, may shed light on its seeming ambiguity. A Connecticut Yankee explores the human capacity for both malice and mercy through the artifice and art of story-telling. From the first pages, the novel draws attention to the power of language to perpetrate violence and to mask it. This paper examines the novel's linguistic and narrative devices, especially the novel's juxtapositions of external differences—a Yankee in medieval England, different dialects, machinery in a pre-industrial age, and so forth—in order to argue that this time-travel tale ultimately reveals more crushing similarities than differences. The novel does not, then, present a linear story-line but rather uses narrative form to explore the overarching theme of human nature, which, regardless of time or of the structure of story, is consistent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".