The Learning Analysis of the Political Text: Structure and Functions of the Election Address (on the Example of G. Zyuganov’s Speeches)
Bibliographic record
Abstract
Good number of researchers have demonstrated the need for online training for faculty members in various countries around the world in recent years. However, most of these academic researchers have discussed the different effects of the online training system. The study deals with the genre structure and formation of a special type of the political text which is an election address of a political leader to the electorate. The article considers the history of the appearance of the public speech genre in Russian political discourse, its functions and linguistic features that solve the problem of revealing the main ideological content at the lexical level. The paper also focuses on the techniques used in this authorial text. They are examined from the perspective of identifying manipulative strategies and tactics of influencing the emotional, rational, and moral-ethical spheres of the electorate, and their implementation at the language level. The research material was the texts of Gennady Zyuganov’s election addresses in 2000 and 2019 taken from the Internet sources, as well as the accompanying comments estimated to be about 50 sources. To increase the degree of objectivity of the results obtained, machine text processing (SEO-type text processing programs, vaal.ru, wordstat.yandex and others) was also used. In the course of the study the linguistic characteristics of the implementations of the political address functions (influence, inspiration, advocacy and propaganda, informing), typical of this type of political statements, are revealed along with the established dynamics of changes in rhetoric by Gennady Zyuganov as the leader of a political party (the Communist Party) and its leading representative.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".