The politics of accountability in Supreme Court nominations: voter recall and assessment of senator votes on nominees
Bibliographic record
Abstract
Abstract While longstanding theories of political behavior argue that voters do not possess sufficient political knowledge to hold their elected representatives accountable, recent revisionist studies challenge this view, arguing that voters can both follow how their representatives vote and use that information intelligently. We apply the revisionist account to the study of Supreme Court nominations in the modern era. Using survey data on the nominations of Clarence Thomas, Sonia Sotomayor, and Elena Kagan, we ask whether voters can and do hold senators accountable for their votes on Supreme Court nominees. While our results for Thomas are ambiguous, we find strong evidence for accountability in the cases of Sotomayor and Kagan. In particular, we show that voters on average can correctly recall the votes of their senators on these nominees, and that correct recall is correlated with higher levels of education and political knowledge. We then show that voters are more likely to both approve of and vote to re-elect their senator if he or she casts a vote on Sotomayor and Kagan that is in line with voters’ preferences. Finally, we show this effect is quite sizable, as it rivals the effect of agreement on other high-profile roll call votes. These results have important implications for both the broader study of representation and for understanding the current politics of Supreme Court nominations.
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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 | 0.015 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".