Lecturer’s Speech Competence
Bibliographic record
Abstract
The analysis of the issue of lecturer’s speech competence is presented. Lecturer’s speech competence is the main component of professional image, the indicator of communicative culture, having a great impact on the quality of pedagogical activity Research objective: to define the main drawbacks of speech competence of lecturers of North-Eastern Federal University named after M. K. Ammosov (NEFU) (Russia, Yakutsk) and suggest the ways of drawbacks corrections in terms of multilingual educational environment of higher education institution. The method of questionnaire was used in the research. The NEFU students took part in the research. The answers to the questionnaire allowed defining the most typical drawbacks for lecturers, working in the multicultural educational environment of region higher education institution. The mentioned drawbacks: words repetition, language rules breaking, wrong vocabulary or pronunciation of foreign words, use of colloquial language, etc. breaking the speech standards and decreasing the quality of lecture material presentation. The authors suggest improving lecturer’s speech competence through the organization of special advanced training courses, business games, discussion platforms, teaching aids and handbooks broadcasting, on-line tutorials, and motivated dialogue mastering as the most effective way of students training process organization.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".