Deixis Role as an Index of Style: A Comparative Corpus Stylistics Analysis of Self, Pakistani and Other Translators
Bibliographic record
Abstract
Deixis are the linguistic expressions which are used for the reference within the context and out of the context to share knowledge. These are functional keys for contextual coherence and for strong involvement of interlocutors. In this dissertation, role of deixis are interpreted with reference of three different categories of novels; Pakistani novels, other translated, and self-translated novels. The aim of this research is to extract the style difference due to the use of deixis as a style indicator in other and self-translated novels in comparison with non-translated novels. Further, we will try to find out how the use of different types of deixis effects on the context of the text. Generally it has witnessed that the other and self-translators are different in their styles. The reason behind such difference is that the self-translator has more freedom while translating their own novels for other audience. For analysis, in total nine novels are selected, three novels of one category and in total three categories are involved. This research adopted the quantitative approach and for analysis corpus software tool, AntConc is used. Whole data is tagged by POS tagger and manually as well, than interpreted through AntConc. The results of this research indicate that the Self translated novels used more deixis and have more simple and direct language then the others translated categories. In these novels the first person and second person communication is more oriented. Similarly other deixis expressions as time, past tense and special deixis are more referred which make language explicit.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".