"Translingual Shakespeare: An Afterword," Shakespeare in Succession: Translation and Time, ed. Michael Saenger and Sergio Costola (Montreal: McGill-Queen's University Press, 2023), 298-307
Bibliographic record
Abstract
Literary translations work with, rather than out of, the space between languages. Translations evolve not only across linguistic and cultural borders but also across time. It is notable that Shakespeare's own play texts feature translational properties that can be amplified in translation. This translingual property makes Shakespeare's text inherently translational in the dramaturgical and gestural senses. A frequently stated myth is that Shakespearean drama is all about its poetic language, and adaptations in another language would violate the "original." The history of performance and reception in and beyond the Anglophone world suggests otherwise. Literary translations rely on, and amplify, the translingual property of languages. Translingual echoes occur when semantically linked phrases mean similar but not identical things in more than one language. Even English-language performances engage in translational behaviors, because audiences would find many scenes confusing without seeing the actors performing them. In our times, most audiences encounter Shakespeare in truncated, often translational, forms, such as short video clips, memes, or quotes. This cross-fertilization and mobility are the norms, not the exceptions. Translation studies contribute site-specific epistemologies to our understanding of what Shakespeare means in different locations and in different times.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".