Emotivity Category as a Means of Popularizing the Political Propaganda Video of the Communist Party of China (CPC)
Bibliographic record
Abstract
The purpose of this paper is to identify the role of the emotional categories in the implementation of political propaganda and the formation of public opinion. The subject of the paper is the peculiarities of the characteristics of the emotional category, the object is the Russian version of political propaganda video "Communist Party of China". The paper adopts descriptive, contextual, and emotive analysis methods. The video is a human-centred text, in which the personalization phenomena are the sources of the emotional category: the personalization in this video (CPC acts as one character) allows us to form a positive, attractive image of the CPC. The verbal (lexical, syntactic means) and non-verbal emotional components of the video (soundtrack, video sequence, color scheme) are identified, the interaction of which contributes to the emergence of a positive emotional impact exerted by the video on the recipient, and the formation of a positive image of the CPC as a great party leading the country to prosperity, which should leave a positive impression on the recipient. In conclusion, we state that emotional categories play a major role in creating emotional impressions, which is closely related to the technique of personalization. The point of innovation of the paper is emphasized the role of this personalized method in popularizing political propaganda.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".