Shortening Agency and Judicial Vacancies through Filibuster Reform? an Examination of Confirmation Rates and Delays from 1981 to 2014
Bibliographic record
Abstract
This Article explores the failure of nominations and the delay in confirmation of successful nominations across recent administrations, with a focus on the November 2013 change to the Senate voting rules. Using a new database of all nonroutine civilian nominations from January 1981 to December 2014, there are several key findings. First, approximately one-quarter of submitted nominations between 1981 and 2014 were not confirmed, with a higher failure rate for the last two Presidents. Nominations to courts of appeals and independent regulatory commissions had much higher failure rates than other entities. Second, for confirmed nominations, the time to confirmation has been increasing. President Obama’s nominees faced confirmation delays that were more than twice as long as President Reagan’s choices. Failure rates of nominations did not always go hand-in-hand with confirmation delays for successful nominations. Although more nominations failed in divided government, confirmation delays were roughly equal when different parties controlled the Senate and the White House. Third, comparing the year after the change to the filibuster rules to the preceding year, confirmation times for the courts decreased but increased for all types of agencies. For many agencies and agency positions, however, significantly fewer nominations failed after the voting change. Even so, these improvements in 2014—to the confirmation rates for both agency and judicial nominees and to the confirmation pace for judicial picks—are relative: for the average nomination, the failure rate was higher and the confirmation process was slower than under preceding administrations. Fourth, nearly 30 percent of nominees hailed from the District of Columbia, Maryland, and Virginia, raising concerns that the confirmation process may be narrowing the pool of top officials. This Article suggests some possible explanations for the findings and further avenues of investigation, and also proposes some reforms.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".