Is pro-Kremlin Disinformation Effective? Evidence from Ukraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Can residents of Ukraine discern between pro-Kremlin disinformation and true statements? Moreover, which pro-Kremlin disinformation claims are more likely to be believed, and by which audiences? We present the results from two surveys carried out in 2019—one online and the other face-to-face—that address these questions in Ukraine, where the Russian government and its supporters have heavily targeted disinformation campaigns. We find that, on average, respondents can distinguish between true stories and disinformation. However, many Ukrainians remain uncertain about a variety of disinformation claims’ truthfulness. We show that the topic of the disinformation claim matters. Disinformation about the economy is more likely to be believed than disinformation about politics, historical experience, or the military. Additionally, Ukrainians with partisan and ethnolinguistic ties to Russia are more likely to believe pro-Kremlin disinformation across topics. Our findings underscore the importance of evaluating multiple types of disinformation claims present in a country and examining these claims’ target audiences.
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.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it