The Application of Functional Linguistic Models for Assessing Quality of Translation: A Descriptive Analytical Study
Bibliographic record
Abstract
This descriptive analytical study aimed at examining the application of linguistic-based functional approaches in assessing the quality of translation. A number of translation quality assessment models were analyzed to investigate the potential of integrating linguistic theories into translation theories. The problem that the present study tackled was that institutions of translation at higher education, translation organizations, and agencies of translation worldwide face difficulties in evaluating the quality of translation. Using objective criteria, which are based on the variables of quality, is still debated among these shareholders. The rationale of the present study is that adopting functional linguistic approaches can help in understanding the components of the quality of translation in terms of the relationship between translation purposes and functional adequacy. Linguistic functional approaches can determine the relationship between textual adequacy, and translation quality of content. Therefore, the current study followed a nonlinear design, which allowed an intensive description and analysis of three functional models applied in Translation Quality Assessment (TQA), and their effectiveness in assessing the quality of translation. Corpus data was collected from the theories and original works of House, Nord, and Colina, on translation quality assessment. Problems related to discourse analysis, function of the language, text typology, and theories of equivalence were examined. Translation criticism and evaluation including the classification of the functional hierarchy of translation, standards and benchmarks, empirical evidence for the success and limitations of the linguistic functionalist approaches in translation assessment, and competences and performances in translation, were thoroughly investigated. The research recommendations of the current study emphasize a number of issues relevant to translation evaluation. These issues are: (a) the significance of integrating the linguistic functional approaches into the curriculum of translation; (b) the importance of defining the components of solid criteria that can be employed for evaluating the quality of translations; and (c) the necessity of providing an empirical tool that can reveal the strengths and weaknesses of translated works. As such, this research study is a contribution in the field of translation evaluation and criticism as it provides a number of models that can be implemented in translation classrooms or in translation organizations. This study also provides an evaluation matrix, based on the models of TQA that can help translators understand the requirements of translation quality prior to the translation process itself. This research is also among the first studies to illustrate how to implement linguistic functional approaches that can be adopted by translation organizations, academic institutions, and publishing houses, to evaluate professional translations and this will inevitably lead to raising the standards of translation quality.
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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 | 0.013 |
| 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".