Bibliographic record
Abstract
Following Newmark, “often, though not by any means always, translation is rendering the meaning of a text into another language in the way that author intended the text” (Newmark, 1988: 5). To accomplish this, finding appropriate and natural equivalents is of prime importance and for the translator; collocation is the most important contextual factor which usually affects translation. So, recognizing whether or not a collocation is familiar or natural is one of the important problems in translation. The Word good because of its broad collocational range, may have different equivalents in different contexts. This paper investigated the different equivalents of the word in the Persian translation of the English novel The History of Tom Jones, a Foundling. In order to fulfill the research purpose, all the instances of the word good, by means of AntConc concordancer and their Persian equivalents were extracted. After collecting data, they were classified and analyzed. Analysis of the data showed that translator, nearly in most cases, aimed at using established and typical equivalents. The study finally comes up with the conclusion that accuracy is no doubt an important aim in translation, but it is also important to bear in mind that the use of common receptor-language patterns which are familiar to the target readers plays an important role in keeping open the lines of communication.
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 | 0.001 | 0.002 |
| 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".