Phraseological Units of the English Language in Newspaper Articles on the Topic of Culture: Structural and Semantic Aspects
Bibliographic record
Abstract
The research examines the features of the structural-semantic approach toward peculiarities of the phraseology of media discourse when studying English. This is an actual problem related to changes in the genre and stylistic palette of modern periodicals. Therefore, it becomes of particular significance to consider the content, classification, and determination of effective means of training specialists, using English-language texts with different stylistic features in their work, regarding the transmission of emotions and content markers in newspaper articles through idioms. The purpose of the research is to determine the dependence of the success rate of students-journalists on introducing innovative methods and thematic blocks to the educational process, as well as establishing an assessment by the participants of the educational process of active study of English phraseology. The research methodology is based on an integrated approach. The descriptive methods, structural and semantic approaches to the classification of idioms were used to study the theoretical material. The experimental method, methods of questionnaires, surveys, observations, were also used in the academic paper. The fundamental hypothesis is that innovative courses containing new approaches and scientific innovations contribute to improving the success and practical skills of students - journalists. The research result is the establishment of interdependence between innovations in the scope and the course content and the improvement of student performance, and interest in learning. In prospect, attention should be paid to a practice-centered approach to the philological component in journalism education in English-speaking countries.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".