Rule 11(E)(1)(C) and the Sentencing Guidelines: Bargaining Outside the Heartland?
Bibliographic record
Abstract
This article examines how the federal sentencing guidelines have affected the practice and substance of plea bargaining. It first examines the nature of Rule 11(e)(1)(C) pleas, which allow a prosecutor and a defendant to agree that a specific sentence is appropriate for the offense of conviction. The judge may accept or reject the sentence, but if she rejects the sentence then she must allow the defendant the chance to withdraw the plea. The article argues that under the federal sentencing guidelines, prosecutors and defendants are increasingly entering into plea agreements that -- although not formally denominated as Rule 11(e)(1)(C) pleas -- are, for all intents and purposes, so binding and constrictive as to any number of relevant facts and terms that they are tantamount to Rule 11(e)(1)(C) pleas. It then examine the effect of this trend in light of the existence of guideline manipulation, which is the practice of prosecutors and defendants agreeing to certain factual and legal stiuplations that affect the ultimate calculation of defendant's sentence under the Guidelines. Building off of the work of Professors Stephen Schulhofer and Irene Nagel, the article then examine the extent to which guideline manipulation exists in the District of Columbia and draws some conclusions about how the increasing familiarity with the Guidelines, as well as Department of Justice policy, have affected the practice.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".