Inflation, Blame Attribution, and the 2022 US Congressional Elections
Bibliographic record
Abstract
Abstract This study investigates the impact of inflation on the 2022 US mid-term elections, a period witnessing the resurgence of inflation as a major concern in the USA for the first time in decades. We develop a pre-registered survey with an embedded experiment to examine the political repercussions of rising prices. We find that individuals experiencing a higher personal inflation burden are more inclined to support Republican candidates. Our survey experiment further assesses the impact of partisan messaging leading up to the election, focusing on two primary narratives: government spending, as emphasized by Republicans, and corporate greed, highlighted by Democrats. The results indicate that attributing inflation to government spending decreases support for Democrats, whereas associating it with corporate greed undermines confidence in the Republicans’ ability to effectively manage inflation. Economic voting behaviour depends not only on objective economic conditions but also on how political parties subjectively frame these conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".