Economic Voting and Political Sophistication in the United States
Bibliographic record
Abstract
The authors propose a reexamination of the conditioning effect of political sophistication on economic voting in U.S. presidential elections. Replicating Gomez and Wilson's (2001) analysis with survey data from the past five American presidential elections (1988—2004), they show that low sophisticates strictly rely on sociotropic economic judgments in their intention to support the incumbent party's candidate. For their part, high sophisticates appear to use both sociotropic and pocketbook evaluations in their voting intention, but only in elections where the sitting incumbent is running for reelection (1992, 1996, and 2004). Most of these findings do not hold, however, once the postelectoral reported vote is used as the dependent variable. Indeed, the authors find that pocketbook evaluations do not have a significant impact on high sophisticates' reported vote choice, and they also find important variance in economic voting effects among low sophisticates. The results indicate that high sophisticates continue to use sociotropic evaluations in their voting decision, but only in incumbent elections. Overall, the analysis raises doubts about some of the previous studies' conclusions and underlines the importance of considering the moderating role of contextual factors such as incumbency and political campaigns in economic voting studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".