Analysing Linguistic Stylistic Devices in The Adventures of Tom Sawyer and So Long a Letter: A Comparative Appraisal
Bibliographic record
Abstract
This research work focuses on linguistic stylistic analysis of Mark Twain’s The Adventures of Tom Sawyer and Mariama Bâ’s So Long a Letter. It aims to identify the various translation procedures used in each novel in order to establish a comparison between the different translation procedures and style of each translator of modern and old English. A sampling method has been used to carry out this research work. Thus, one extract has been selected with its corresponding translation from the French and English versions of each novel. The results show that, in The Adventures of Tom Sawyer, the translator has used predominantly adaptation for his translation representing 32.32% in both selected extracts whereas in So Long a Letter, the translator has adopted predominantly literal translation representing a proportion of 28.48% in order to preserve the sustained register of the source text. However, both translators have also used other translation procedures in lower proportions depending on the context orientation. It has been noted that translation methods such as calque has been used only once whereas borrowing is nonexistent in the selected extracts from both literary works.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".