Bibliographic record
Abstract
Malapropisms have received little specific attention in studies concerning the translation of humorous phenomena, as researchers have usually addressed the broader category of wordplay. Malapropisms, however, while a subtype of wordplay, also represent a phenomenon in their own right, and their longstanding use as a humorous device in literature, as well as the particular translation problems they pose, largely justify a separate analysis. Additionally, more often than not, the translation of malapropisms has been addressed from a prescriptive point of view. Therefore, in addressing the translation of malapropisms in the Spanish versions of Joseph Andrews, this paper has a double aim. Firstly, it seeks to highlight the need for a comprehensive framework of analysis capable of singling out the particular features of malapropisms within a given text, paying attention most notably to their function in the text as a whole, their typological range, and the translation techniques employed to deal with them, as well as some extratextual factors that may help explain certain decisions taken by translators and their degree of acceptance within the target literary system. Secondly, it draws attention to descriptive analysis, showing how, by improving knowledge of the phenomena involved, it can prove useful for further translations.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".