Reconstructing collaborative (self-)translations from the archive: The case of Samuel Beckett
Bibliographic record
Abstract
When literary authors translate their own work, they sometimes collaborate with other writer-translators. While such “collaboration” is often acknowledged on the title pages of the resulting publications, the nature of each joint venture is typically very different in practice. Surviving archival traces often allow for a more detailed reconstruction of the varying working methods that were adopted for every co-translation, but it would be naïve to assume that even the most completely preserved record will make it possible to conclusively identify the function of every participant in the creative process. In this article, we will combine genetic criticism and genetic translation studies on the one hand, with microhistorical and social approaches to translation on the other, as complementary methodologies to further investigate the understudied notion of collaborative (self-)translation. By using as our test case the extant draft versions and other related materials that document the collaborative relationships between Irish bilingual author Samuel Beckett and his co-translators in French, English and German, the purpose is to show that a process-oriented and interdisciplinary approach to translation can help overcome some of the challenges and limitations presented by digital editions and archives such as the Beckett Digital Manuscript Project (BDMP).
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.031 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".