The Learning Analysis of the Political Text: Structure and Functions of the Election Address (on the Example of G. Zyuganov’s Speeches)
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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