“We Will not Get Another Chance if We Lose This Battle Now”: Populism on Ukrainian Television Political Talk Shows ahead of the Presidential Election in 2019
Bibliographic record
Abstract
Against a background of increasing electoral support of populist political actors in Europe and beyond, this study offers an exploratory inquiry into modern Ukrainian populism. The article examines populist communication, broadcast on the most highly rated Ukrainian television political talk shows, on the eve of the 2019 presidential election, which was completed in two rounds. A qualitative content analysis of populist communication acts (n=283) shows that Ukrainian viewers were exposed to diverse political discourses containing empty, anti-elitist, emergency, and complete populism, depending on which channel(s) they watched. The dominance of one or another type of populism on the studied channels mirrors the dynamics of media-political parallelism typical of Ukrainian commercial television. The study also examines the roles of different actors—moderators, journalists, and politicians—in either restricting or facilitating populism in the talk show studios. The populism-related reactions collected during this analysis (n=145) are discussed through the prism of normative roles, with a focus on gatekeeping, interpretation, and initiation. Implications for the stakeholders involved in the process of production, moderation, and consumption of political talk shows are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".