The role of mental translation in learning and using a second/foreign language by female adult learners
Bibliographic record
Abstract
The purpose of this study is to ponder some theoretical considerations concerning a predominant phenomenon of mental translation (MT) observed by second language teachers and learners in learning and using a second/foreign language. The sample available to the researcher consists of seven female adult learners speaking different languages. The choice of the sample is not related to the age level of the learners, but their English level (5-6) according to the Canadian Language Benchmarks. The research instruments used in this study are two compositions written on two occasions, during 2013-2015 and in 2016, a personal questionnaire, a strategy questionnaire, and a structured face-to-face interview. The results show that learners use mental translation as a strategy to compare and establish similarities and differences between first language (L1) and second language (L2). The learners create a translational zone where they put the results of the comparisons between L1 and L2 (MT product). The MT product becomes a procedural knowledge, stored in the long-term memory. To write in L2, the learners retrieve the information from their translational zone automatically and without awareness. The type of errors made in both written compositions shows how the influence of the mother tongue prevails despite the time living in the second language country, or the study within an academic system or with tutors. The use of mental translation as a strategy and as a product can explain the errors made by the learners in the second language and the prevalence of the influence of L1 in learning and using L2.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".