Linguistic Humor in the Select Works of Mark Twain, Stephen Leacock, and R.K. Narayan
Bibliographic record
Abstract
This paper explores the use of linguistic humor in the select works of Mark Twain, Stephen Leacock, and R.K. Narayan, three literary giants renowned for their distinctive comedic styles. Through a detailed analysis, the study examines how these authors employ various linguistic techniques to elicit humor, including wordplay, satire, irony, and parody. Mark Twain's sharp wit and mastery of dialects, Stephen Leacock's whimsical and absurd scenarios, and R.K. Narayan's subtle and culturally rich narratives serve as primary examples of their unique approaches to humor. By delving into specific texts, this research highlights how linguistic choices contribute to the comedic effect and the overall impact on readers. The paper also considers the cultural and temporal contexts that shape each author's humor, providing a comprehensive understanding of their contributions to literary comedy. Through comparative analysis, the study underscores the universal appeal and enduring relevance of linguistic humor across different cultures and time periods. This investigation not only celebrates the art of humor in literature but also offers insights into the broader implications of comedic expression in understanding human nature and societal norms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".