Discourse Presentation as an Index of Style: A Comparative Corpus Stylistic Analysis of Self and Other Translators
Bibliographic record
Abstract
This study aims to explore the style of other and self-translators in comparison with non-translated texts, assuming discourse presentation as an indicator of style. Theoretically, other and self-translators are considered different in their translation style. The reason is that self-translators enjoy more liberty and authority over the source text as compared to other-translators (Bozkurt, 2014; Cordingley, 2013). However, practically, previous studies have explored either the style of self-translators (Ehrlich, 2009) or other-translators (Saldanha, 2011). None of the studies has provided a comparison among these types. The current study is a pioneer in establishing general styles of self and other-translators. It explores three categories of literary texts i.e., by self-translators, other-translators and by Pakistani writers. Each category further comprises of three representative texts. They are, then, processed through AntConc 3.4.4 and tagged manually. The model of speech, writing and thought presentation proposed by Semino & Short (2004) based on Leech & Short’s (1981) model is used, as it encompasses all the presentation techniques employed in literary texts. Frequencies acquired through tagging are then normalized and results are presented in the form of graphs. Findings of the research reveal that both other and self-translators are character-oriented in their style. However, other-translators are more objective and reader-oriented with less interference from the narrator. In contrast, self-translators are more subjective with more intervention from the narrator. These results are significant for further researches concerning self and other-translators.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".