Digital Tools in Teaching the Mass Media Language
Bibliographic record
Abstract
The functioning of language in modern media is a complex set of different types of discourses. It involves using mental and cultural codes, concepts and archetypes, taking into account the specifics of Internet content and methods of its promotion, along with traditional newspaper journalism, knowledge of the basics of cognitive, communicative and information-theoretical theories and methods, etc. The purpose of the academic paper is to clarify the features and modern tendencies of teaching the mass media language with the help of digital tools, as well as to establish particular practical aspects of using such educational means in the process of teaching the mass media language. In the course of the research, the analytical-bibliographic method was used to study the scientific literature on teaching the mass media language with the help of digital tools. Along with this, induction, deduction, analysis, synthesis of information, system-structural, comparative, logical-linguistic methods, abstraction, and idealization were applied for studying and processing data. At the same time, the questionnaire survey was conducted in online mode by the research authors to practically clarify certain aspects of using digital educational tools in teaching the mass media language. Based on the research results, the primary and most significant theoretical aspects of the process of teaching the mass media language using digital educational tools were highlighted. Moreover, the standpoints of education seekers and teachers of higher educational institutions regarding the key aspects of this issue were clarified.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".