“To What Song Does the Vein of Poetry’s Lute Throb?” The Hypotactic Poetics of Bīdil’s Persian ghazals in Sanskrit-Hindi-Arabic Meters
Bibliographic record
Abstract
Abstract How, despite the non-specificity of the ghazal’s semantics, can its rhythms contribute to intellectual history? This essay proposes an answer to this question with reference to sixty of the almost three thousand Persian ghazals composed by ʿAbd al-Qādir “Bīdil” (1644–1720) of Delhi. These sixty are distinguished by the fact that their meters are rare or unprecedented in Persian but common in either Arabic or Sanskrit-Hindi or both. Building on the rare aural commonalities between these sixty Persian ghazals and Arabic and Sanskritic poetry in corresponding rhythms, this essay argues that Bīdil used them to multiply and complicate relations between the ghazal’s speakers and its addressees by amplifying hypotaxis; and to subsume the devotional mood of prosodically identical but paratactically simple Hindi hymns to a monist imagination. It concludes by suggesting that his Sanskrit-Hindi-enabled hypotaxis and rhythms in Persian were stylistic imitations of God’s hierarchized self-disclosures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".