Bibliographic record
Abstract
Abstract Plays on words , humorous or not, pose serious challenges to the translator, as Dirk Delabastita (1997) , among many others, has stated. Self-translators know the intended communicative effect behind the puns, giving them a privileged perspective regarding their translation. This is the case with author Nancy Huston, born in 1953 in Alberta, Canada, and residing permanently in France since 1973. She started using self-translation consistently after discovering she could thereby improve her work, which regularly includes wordplays. This work looks at puns from two of her earlier self-translated novels, comparing Huston’s English and French version to see how she deals with the complexity they entail. Following Delabastita’s typology of puns ( 1996 : 128, 2014 : 604), Huston’s puns are both vertical and horizontal wordplay, display paronymy and homophony as well as many more aspects. This chapter comments on cases of pun-to-pun translation, pun-to-alliteration, pun-to-no pun, etc. Huston herself claims she desires the two versions to be as alike as possible ( Mi-Kung YI, 2001 ), and this article aims at confirming this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 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; both teacher heads agree on what is shown here.
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".