Dis-contenting Khayyam in the Context of Comparative Literature: An Invitation to Translating Rubaiyat with a Focal Shift from Content to Form
Bibliographic record
Abstract
Since its conception in France in 1877, Comparative Literature, always subject to a critique of Eurocentrism, has been in a state of perpetual crisis. In “The Old/New Question of Comparison in Literary Studies: A Post-European Perspective” (2004), Ray Chow argued for a Post-European perspective in which comparatists begin with the home culture and look outwards to the European cultures, contrary to the dominant approach of doing just otherwise. Missing in Chow’s argument is the position of translation in this post-European perspective. In the 14 years between 2004 and 2018, the grandiose claims of comparative literature have been problematized and addressed; the lay of the land, however, remains predominantly Eurocentric, as it still focuses on content disproportionately. In this paper, through a study of English translations of Khayyam’s Rubaiyat, and taking Chow’s argument further, I argue that with its commitment to transfer the form of a text as much as the content, translation studies can further help comparative literature to distance itself from Europe. To exemplify the implication of this, I suggest that a translation of Khayyam’s Rubaiyat from Farsi to English would be more faithful to the original if its translations were to focus on the poem’s form rather than the content. I argue that translating with a focus on form would foreignize Khayyam’s poetry, hence an act of resistance against cultural hegemony.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".