Introduction: what is failure in translation?
Bibliographic record
Abstract
This special issue is the brain child of the recent volume Vergil and His Translators (Oxford University Press, 2018) which was edited by the guest editors. More specifically, Craig Kallendorf’s essay in the volume, ‘Successes and Failures in Vergilian Translation’, which we placed in prime position, opened a new avenue of discussion that was only tangentially explored in that volume. In this collection of essays for the Classical Receptions Journal we explore this theme further as it relates to translation studies and Vergilian studies today. The essays of seven international scholars that comprise this special issue explore different manifestations of failure and address reasons for the failure of even the most accomplished translators to bring Vergil’s masterpieces to the reading public in their cultures. Initially, this line of exploration may seem to be a futile exercise: what is gained from talking about translations that failed to accomplish the only goal for which they were conceived, namely making Vergil’s poems accessible to readers in another culture who cannot read Latin? If the translation fails in delivering in some tangible ways the essence and literary qualities of the original poem, then that translation deservedly must disappear into the river of literary oblivion. However, as these essays show, the aesthetic of any given translation is also firmly rooted in the society of the target language. These essays reveal that failed translations can sometimes tell us more about the aesthetics, culture, politics, and ideology of society than the successful ones can, and they have the potential to provide a fertile ground from which more successful translations might grow.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".