Bibliographic record
Abstract
Abstract This rejoinder proceeds in a dialectical fashion. For the sake of argument, the first part takes the opposite view of the thesis according to which the translator’s identity must modify the parameters of translating different forms of humour (comedy, satire, irony, wordplay). Upon reflection, it appears that self-translators also face the constraints inherent in this exercise, whether broader cultural transfers need to be re-contextualized or specific aspects raise questions of a linguistic or rhetorical nature. This observation allows for a clearer appreciation of the specificity of self-translation in a second part of the demonstration, where received notions such as agency or intention are examined anew. Agency is not unlimited, nor is access unmediated, but both are part of the cocktail of self-translation, whose main originality resides elsewhere, however: in the possibility of embarking on a translation before completing the first text, that is, self-translating “simultaneously.” The demonstration is illustrated with many examples, and due notice is taken of articles in this volume that deal with self-translated texts and their (re)writers.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".