Issues and prospects of teaching Russian vocabulary in the first quarter of the 21st century
Bibliographic record
Abstract
The article examines the current issues of teaching lexicology in Russian language lessons in modern schools and identifies development prospects for the methodology of lexicology in the 21st century. The article aims to summarise the achievements of methodology since the introduction of the "Vocabulary and Phraseology" section into school curricula in the 20th century, which is associated with the scientific and pedagogical activities of Professor M. T. Baranov. Another goal is to show how to implement his ideas in the paradigm of modern education. The paper focuses on the importance of developing logical and figurative thinking when studying lexicology. With this end in view, the comprehensive school curriculum is analysed. In it, the "Lexicology" section is present only in years 5 and 6. Moreover, the study draws attention to the content deficiencies associated with underestimating the available significant scientific and practical experience of teaching word meaning interpretation, as well as the problem of enriching the modern youth’s lexicon with vocabulary related to values. The assignments on lexicology proposed for completion are designed according to the methodological principle of graduality. The tasks are based on the axiological and cognitive-pragmatic approaches within the framework of teaching functional semantics. The study employed the descriptive method for historical and logical analysis of M. T. Baranov’s works and the modelling method when constructing a system of graded exercises. A future research line is developing a functional vocabulary teaching model for secondary general education schools.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".