Last Words: A Survey and Analysis of Federal Judges' Views on Allocution in Sentencing
Bibliographic record
Abstract
Allocution — the penultimate stage of a criminal proceeding at which the judge affords defendants an opportunity to speak their last words before sentencing — is a centuries-old right in criminal cases, and academics have theorized about the various purposes it serves. But what do sitting federal judges think about allocution? Do they actually use it to raise or lower sentences? Do they think it serves purposes above and beyond sentencing? Are there certain factors that judges like or dislike in allocutions? These questions — and many others — are answered directly in this first-ever study of judges’ views and practices regarding allocution.The authors surveyed all federal district judges in the United States. This Article provides a summary and analysis of the participants’ responses. Patterns both expected and unexpected emerged, including, perhaps most surprisingly, that allocution does not typically have a large influence on defendants’ final sentences. Most of the judges agreed, however, that retaining this often-overlooked procedural right remains an important feature of the criminal-justice process.This Article also synthesizes judges’ recommendations for both defendants and defense attorneys aiming to craft the most effective allocution possible. Critical factors include preparing beforehand, displaying genuine remorse, and tailoring the allocution to the predilections of the sentencing judge.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".