‘While there’s a breath in my body’: The systemic effects of politically motivated retirement from the Supreme Court
Bibliographic record
Abstract
Many observers of the US Supreme Court suspect that justices time their departures from the Court based on ideological and political factors. This paper assesses the theoretical effects of such behavior. Does political timing of retirement devalue the appointment process and thereby make the Court less responsive to the public? Do politically motivated retirements lead to more justices serving beyond their productive years? Based on a formal model of retirements, we find that politically motivated retirements have little effect on political influence on the Court because, on average, for every liberal justice who declines to retire because there is a Republican president, there is a conservative justice who retires early because there is a Republican president. The model also implies modest, but non-linear effects of politically motivated retirement on the age composition of the Court as small amounts of such behavior leads to an older Court, but large amounts of politically motivated behavior lead to a younger Court. Imposing term limits on justices would increase responsiveness to electoral outcomes, lower the age of justices and dramatically increase Court turnover.
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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 | 0.003 | 0.016 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".