Does Analytic Thinking Insulate Against Pro‐Kremlin Disinformation? Evidence From Ukraine
Bibliographic record
Abstract
Pro‐Kremlin disinformation campaigns have long targeted Ukraine. We investigate susceptibility to this pro‐Kremlin disinformation from a cognitive‐science perspective. Is greater analytic thinking associated with less belief in disinformation, as per classical theories of reasoning? Or does analytic thinking amplify motivated system 2 reasoning (or “cultural cognition”), such that analytic thinking is associated with more polarized beliefs (and thus more belief in pro‐Kremlin disinformation among pro‐Russia Ukrainians)? In online ( N = 1,974) and face‐to‐face representative ( N = 9,474) samples of Ukrainians, we find support for the classical reasoning account. Analytic thinking, as measured using the Cognitive Reflection Test, was associated with greater ability to discern truth from disinformation—even for Ukrainians who are strongly oriented towards Russia. We find similar, albeit weaker, results when operationalizing analytic thinking using the self‐report Actively Open‐Minded Thinking scale. These results demonstrate a similar pattern to prior work using American participants. Thus, the positive association between analytic thinking and the ability to discern truth versus falsehood generalizes to the qualitatively different information environment of postcommunist Ukraine. Despite low trust in government and media, weak journalistic standards, and years of exposure to Russian disinformation, Ukrainians who engage in more analytic thinking are better able to tell truth from falsehood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".