The Use of Linguistic Landscape as a Training Resource for Developing Students’ Translation Competence
Bibliographic record
Abstract
This study is an attempt to investigate the efficacy of using linguistic landscape scripts as a training material to develop student translators’ translation competence. Focusing on the sub-competences stated in PACTE’s model of translation competence, namely the bilingual, extra-linguistic, strategic and knowledge about translation sub-competences, the study, based on the participants’ reflective feedback and instructor’s observations, aims to determine whether the use of bilingual public signs with translational content can help translation students develop their translation competence. The qualitative data used in the study were collected via focus group discussions, instructor’s observations and a survey to obtain students’ perspectives. The study found out that through discussion, evaluation and reflection on authentic materials taken from students’ environment, students can raise their bilingual, procedural and strategic awareness, which contributes to development of self-confidence in decision-making and problem-solving skills in the translation process. Furthermore, the study revealed that the use of such authentic experiential materials can provide students with a range of practices, actors and factors related to the process of translation that enhances their extra-linguistic competence and knowledge about translation, which eventually enables them produce translations that conform to the standards of meaningfulness, appropriateness and correctness required by the target audience.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".