ESP for Interpreters and Translators: Foreign Language Provision or Integrated Education?
Bibliographic record
Abstract
The paper is a complex study of advanced training of state employees, including servicemen on the positions of interpreters and translators. The research is aimed at efficient organization of English for Specific Purposes course taking into account specific needs of the main stakeholders. The purpose of the training is the efficient use of English by course participants, who are Philology specialty graduates, in professional settings. The learners are expected to gain knowledge and experience in the positions of interpreters or translators of the Division of International Cooperation, the Division of Military and Technical Cooperation, the Division of Informational and Analytical Support of state bodies of Ukraine. On the basis of the insights gained from designing an ESP course for state employees on such positions, analyzing their job responsibilities and all the stakeholders’ needs, reviewing literature on ESP and integrated education, this paper is intended to provide theoretical background and practical assistance for ESP specialists involved in research and design processes of the courses as well as for those who prepare learning materials for interpreters and translators seeking problem-based training to develop their professional competence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".