The Interdisciplinary Character of Research into the Translation of Literary Irony
Bibliographic record
Abstract
In this article, while I welcome the call for a more interdisciplinary character, I also endorse the idea that the methods of neighbouring disciplines do not necessarily need to be included into one comprehensive research model for TS. The advantages of interdisciplinary research are illustrated with research into the translation of literary irony. In the first part of the article, I present an analytical instrument for comparative research between original and translated ironic excerpts. I will demonstrate that by including insights from, mainly, pragmatic and cognitive approaches to irony, I have been able to fine-tune the three-part analytical instrument called “the ironic effect.” Its advantages and heuristic scope are illustrated with excerpts from La tía Julia y el escribidor (Mario Vargas Llosa). In the second part of the article, I discuss the analyses of two other novels, Tres tristes tigres (Guillermo Cabrera Infante) and La invención de Morel (Adolfo Bioy Casares) and show that, by adopting very different research hypotheses and multiplying the questions asked, the observed data were better understood. I conclude that there is margin for an inclusive, open and flexible TS methodology, provided that both theory and methodology are understood as means of understanding. Stripped of its ontological status, theory, then, is nothing but a functional notion.
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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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".