Two Implications of Survey Research Mode during War: Evidence from Russia's Full-Scale Invasion of Ukraine
Bibliographic record
Abstract
Conducting social science research during an active war raises distinct challenges. To investigate the relationship between mode and both coverage and social desirability bias during wartime, I conduct a multi-mode study (web, telephone) with two independently drawn samples of the Ukrainian population during Russia's full-scale invasion of Ukraine in the summer of 2022. I employ identical demographic and behavioral questions in both surveys to examine coverage bias and pre-register a framing experiment to investigate social desirability bias in reported in reported volunteering activities after the onset of the full-scale invasion. Observationally, I find that because web studies cannot reach the oldest and most rural Ukrainians, they likely contain much more significant coverage bias with respect to war-related demographic variables compared to the telephone sample. Experimentally, consistent with other studies that report greater social desirability bias in interviewer-mediated models, I find evidence of inflation in reporting volunteering activities only in the telephone survey. Our results demonstrate that, given modern survey techniques, wartime attitudes and behavior in Ukraine can be reliably measured, but there is a trade-off in survey modes between coverage bias and social desirability bias.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".