Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it