Imitation and Innovation in Timurid Poetics: Kashifi's<i>Badāyiᶜ al-afkār</i>and its predecessors,<i>al-Muᶜjam</i>and<i>Ḥadāᵓiq al-siḥr</i>
Bibliographic record
Abstract
Husayn Vaᶜiz-i Kashifi, The Persian Prose-Writer Par Excellence of the ninth/fifteenth century, did not compose his treatiseBadāyiᶜ al-afkār fī ṣanāyiᶜ al-ashᶜār[Wondrous Thoughts on Poetical Tropes] to compensate for the lapses of his predecessors, or to make a groundbreaking contribution to the Persian literary sciences. His stated purpose, at least, was rather prosaic.Badāyiᶜ al-afkārwas Kashifi's calling card to the new Timurid ruler Sultan-Husayn Bayqara (r. 873/1469–911/1506), which—so he hoped—would secure his place among the literary experts at the Timurid court. That much we know from the author's introduction to the treatise. At the time of Sultan-Husayn Bayqara's ascension to the throne, Herat was at the peak of its glory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".