How to Survey About Electoral Turnout? The Efficacy of the Face-Saving Response Items in 19 Different Contexts
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
Researchers studying electoral participation often rely on post-election surveys. However, the reported turnout rate is usually much higher in survey samples than in reality. Survey methodology research has shown that offering abstainers the opportunity to use face-saving response options succeeds at reducing overreporting by a range of 4–8 percentage points. This finding rests on survey experiments conducted in the United States after national elections. We offer a test of the efficacy of the face-saving response items through a series of wording experiments embedded in 19 post-election surveys in Europe and Canada, at four different levels of government. With greater variation in contexts, our analyses reveal a distribution of effect sizes ranging from null to minus 18 percentage points.
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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 | 0.037 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it