Stylistic techniques to generate humor: an analysis of humorous instructive examples cited in the <i>Gardens of Magic</i>
Bibliographic record
Abstract
Abstract This article scrutinizes the utilization of stylistic devices for the generation of humor in literature, with a particular focus on Ḥadāʾiq al-Siḥr fī Daqāʾiq al-Shiʿr (Gardens of Magic in the Minutiae of Poetry), authored by Rashīd al-Dīn Waṭwaṭ (d. 1182). Functioning as a comprehensive guide to figures of speech and literary eloquence, Ḥadāʾiq al-Siḥr employs examples from both Arabic and Persian literature to elucidate its principles. While primarily devoted to panegyrics, Ḥadāʾiq al-Siḥr does not disregard humor, employing humorous samples to clarify the subtleties of this genre. Waṭwaṭ, adhering to the medieval pedagogical tradition, furnishes concise explanations coupled with multiple illustrations, demanding an in-depth analysis of instructive examples to unveil their intricacies. Employing the script-based theory of analyzing humor, this study scrutinizes humorous instances within Ḥadāʾiq al-Siḥr, providing insights into Waṭwaṭ’s approach to comedic elements in literature. Beyond this, the article explores the foundational aspects of humor creation within the medieval literary conventions of Persian and Arabic, thereby contributing to a nuanced comprehension of this literary genre.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".