Simulation-Based Learning as an Effective Method of Practical Training of Future Translators
Bibliographic record
Abstract
The research topicality is determined by the problem of lack of qualified specialists who have a high level of foreign language proficiency and the ability to carry out effective professional foreign language communication. The study involved the following methods: Rokich’s Value Orientations Test, Nemov’s methods for diagnosing the expectation of success level, the Self-Efficacy Scale (R. Schwarzer, M. Jerusalem); testing on the material taught on the Theory and Practice of English Translation, chi-squared test, Mann-Whitney U test. Results: Simulation of real conditions and situations of translation activity is used in almost every lesson (80%), promoting the development of future translators’ professional competencies. The final control in the experimental group found that all students had a high (48.10%) or medium (51.30%) level of foreign language proficiency, which confirms the effectiveness of the simulation method. In the experimental group, the percentage of students with a low level of foreign language proficiency at the end of the research decreased from 26.3% to 0.6%, and the percentage of students with a high level of foreign language proficiency almost tripled. At the same time, in the control group the number of students with a low level of foreign language proficiency decreased from 25% to 10%, while the percentage of students with a high level of foreign language proficiency increased by only 1.6 times. Therefore, the hypothesis of this scientific research was experimentally confirmed. Simulation training promotes the development of foreign language competencies of students majoring in Translation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".