Bibliographic record
Abstract
There are number of methods, approaches and techniques in evaluating translated texts, but none is as systematic as House (1977, 1997, 2004b, 2007, 2009, 2013, 2015) model of translation quality assessment. The present study was carried out to explore House (1997) model of translation quality assessment (TQA). To pursue this purpose, the model was applied to a psychology textbook. The model is based on functional equivalence and is set to give a thorough analysis of the source language, target language and their comparison. Researchers, however, came to realize some shortcomings in the model; there are basically couple of difficulties with the model. First, House (1997, 2015) does not provide any guide to how much translated text should be assessed i.e. what sample size is needed for the model to operate and be reliable. Another even more serious problem is the fact that one cannot spell out exactly to which degree quality of target text is equal to its source? The researchers proposed two main components to be added to the model, one to the beginning and the other to the end of the model. In order to modify this model in terms of sample size and rating scales, researchers decided to adopt Sical system, which stands for the Canadian Government Translation Bureaus Quality Measurement System in order to have a scale for final quality statement. According to Sical measurement system, a passage of 400 words has been randomly chosen from a psychology textbook and tested against the modified model of quality assessment. Researchers found that the translation could be ranked B fully acceptable. The result shows that the modified model is more systematic and accessible.
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 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 | 0.086 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".