Assessing <i>tu quoque</i> arguments using real-world data: media coverage of the start of Russia’s war in Ukraine
Bibliographic record
Abstract
This article assesses the tu quoque version of the ad hominem fallacy by comparing media coverage of the war that began on February 24, 2022, in Russia, Ukraine, and the United States with speeches by Russia’s President Putin. The contents of newsfeeds covering the war, produced by three major media outlets—Gazeta.ru, Ukrainska Pravda, and CNN—were analyzed during the first seven weeks of the war. It is shown that the reasons for waging war stated by Russia’s President Putin diverged from messages propagated by the Russian media outlet during the first month and a half of Russia’s large-scale invasion of Ukraine, despite the tight control exercised by the government over mass media. Neither the Ukrainian nor American media outlets picked up Putin’s arguments. This situation indicates a fallacious character of Putin’s rhetoric.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".