Spin Doctoring in modern political discourse: linguistic aspect
Bibliographic record
Abstract
In the article, the realization of verbal influence (also known as suggestion) phenomenon in political discourse is considered. This concept is defined as a whole combined image of the text itself and emotions of its recipient and addressee,including the peculiarities of perception, external and internal circumstances, its pragmatic and linguistic aspects, etc, and is aimed at a a political subject’s influencing a political object. Usually, political discourse and its immanent influential properties are researched from the standpoint of Psychology, Communicative Linguistics, Pragmalinguistics, Political Linguistics and other related sciences, but the author proposes to involve Neurolinguistic Programming as a modern science which deals with analyzing the peculiarities of perception, processing and generation of information and its transformations from deep thought structures to superficial speech; as well as Spin-Doctoring, an ultramodern complex discipline aimed at correcting the negative image of the event in the media after its has already happened or right before its manifestation, which is impossible without the involvement of language techniques to influence the recipient or a group of them. In order to fully research the political discourse, which in the network of this article is represented by the political speeches of the leaders of Ukraine, USA, France, Spain, Italy, Canada, Germany, the author involves both the basic Spin Doctoring techniques of political discourse correction ( negative information delay, ambiguous informing, focus switch, interspersing the artificial situations with elements of naturalness, and the technique of controlled information leaks and preparation for events expectations); and the NLP paradigm meta- and Milton-model analysis text having been utilized in order to isolate the actual linguistic influential patterns (markers of language metamodel processes, simple, complex and indirect inductions). The analysis details the available information on the peculiarities of implementing and enhancing the linguistic influence within the political discourse, as well as outlines the crucial next steps in the further researches on this topic, especially ones in the field of Ukraine’s state information security, which is a particularly important aspect of the state’s modern information during hybrid wars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".