“Kyiv regime,” “junta,” “neo-Nazis,” “ethnic Jew”: discursive derogation of Ukrainian authorities and enemy-other constructions in Vladimir Putin’s speeches
Bibliographic record
Abstract
In this paper, the author analyzes the discursive strategies of derogating Ukrainian authorities used by Vladimir Putin and their persuasive impact on shaping public opinion and social cognition. Using ideological discourse analysis, she investigates Putin’s tactics for reinforcing negative attitudes towards the “Kyiv regime” in his speeches since 2014. Focusing on derogatory labelling, semantic implications, and pragmatic implicatures, this paper outlines Putin’s discursive devaluing and dehumanizing of the Ukrainian authorities, justifying, first, the annexation of Crimea and, eight years later, the invasion and war in Ukraine. The analysis aims to reveal the persuasive potential of derogatory language with its semantic manipulation and capacity to format public opinion and social cognition, contributing to the categorization of “the out-group” as an “enemy-other.” It is crucial not only for legitimizing certain policies but also for constructing collective self-identity. Revealing a deliberate shift from subtle insinuations to pronounced adversarial portrayals over time, the conflictive othering of Ukrainian authorities continues to evolve.
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 | 0.001 | 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 | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".