The Man of Wiles in Popular Arabic Literature: A Study of a Medieval Arab Hero * By MALCOLM C. LYONS
Bibliographic record
Abstract
One of the most fascinating aspects of Arabic popular epic is the ʿayyār, the trickster character who plays such an important role in most epics. In the introductory volume of his The Arabian Epic (1995), the book that did so much to make the genre of popular epic accessible to scholars, Malcolm Lyons called him the ‘man of wiles’, the equivalent of the metis Odysseus of Homer. Pages 118–27 of The Arabian Epic, Vol. I, is devoted to this type of narrative agent, one of whose functions (says Lyons) is to act as adversary to the villain of the story. Lyons sums up a number of striking characteristics that may, in various combinations, be found in ʿayyār characters: wiliness, versatility, being a master of disguise, speaking many languages, having dirty personal habits and disgusting table manners, lacking a sense of honour, stinginess, having no qualms about breaking promises, being a fast runner. The ʿayyār’s weapon often is the bow-and-arrow, an un-heroic weapon. Sometimes the ʿayyâr possesses supernatural powers and can change his shape at will. As Lyons remarks at the end of this section: ‘The purpose of the Man of Wiles has yet to be discussed.’
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".