Replication Data for: 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 Active 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 post-communist 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 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 | 0.024 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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".