Retelling as Resistance: Translating Nizāmī’s Ḳhusrau o Shīrīn in Colonial India
Bibliographic record
Abstract
Abstract Urdu literature written in the second half of the nineteenth century in North India has often been characterised in terms of rupture, reshaping, and loss of the legacies of the past. In this essay, I argue that such literary-historical framing not only limits our understanding of thriving Urdu aesthetic production in colonial North India, but also misrepresents a vibrant literary culture that continued to resist the colonial aesthetic valuations by drawing on the legacies of the past much more aggressively. To demonstrate this, I examine here Munshī Gobind Prasād Faẓā’s striking Urdu retelling of Niz̤āmī Ganjavī’s Persian poem, Ḳhusrau o Shīrīn, in the larger context of the colonial aesthetic politics. Through examining Faẓā’s maṡnavī (a poem in rhyming couplets) in the framework of anti-colonial resistance, I argue that Urdu maṡnavī writers engaged the notion of temporality through drawing on the deep memory of the Persianate past in the form of the maṡnavī and presented it as a counter-narrative to the poetics of orphaned newness that were becoming a gold standard in other literary forms at the time. In this essay, I highlight the unique cultural work that the form of Urdu maṡnavī did in addressing, and even overcoming, the proverbial death of a literary culture that remained thriving till deep into the twentieth century.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".