English against Englishing: The Case of an Early English Translation of an Oriya Novel1
Bibliographic record
Abstract
Successive translations of a text mirror the shifting translatory practices of a culture. Paradigms for/of translation can be tracked by following the trajectory of these translations. Usually, however, the “translative turn” is read off from the latest in the series of translations inspired by a text. It is the other way round with the translated Oriya novel, Fakir Mohan Senapati’s Chhamana Athaguntha (1902), which is an exception to this developmentalist rule. An early English translation of the novel titled The Stubble under the Cloven Hoof (1967), produced by C.V.N. Das, shows a highly visible and active translator. In this Das uses English to counter the Englishing tendencies that are the inevitable end result of his attempt, as he says, at “rechristening” a vernacular tale. This essay demonstrates this and also explains the related phenomenon of the foregrounding of the task of the translator.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".