Bibliographic record
Abstract
This contribution is based on the thesis of a connection between media scandals, political scandals, including the scandalization of society, and the language used in everyday settings. Different kinds of scandals in politics, the media, and society have been common in the Russian public sphere. This could be seen as the preparation of an atmosphere in which overaggressive, demagogical communication is valued, where victims are portrayed as self-inflicted or even become the “real perpetrators.” This article aims to explore the extent to which this kind of scandalization and the provocation associated with it diffuse into social discourses, for example, everyday language. To do so, the characteristics of scandalous language are first elaborated based on the literature and a small corpus of media coverage of scandals. Then, data from YouTube with political as well as non-political content is examined to see if the same linguistic mode is used in these different communicative areas. The comments and discussions are analyzed in terms of their use of language, with a focus on the lexical and other indicators of a language of scandal. Its existence, dissemination, and linguistic integration are key to finding hints of a more general preference for provocative language.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".