Mandatory Sentencing and Racial Disparity: Assessing the Role of Prosecutors and the Effects of Booker
Bibliographic record
Abstract
This Article presents new empirical evidence concerning the effects of United States v. Booker, which loosened the formerly mandatory U.S. Sentencing Guidelines, on racial disparities in federal criminal cases.Two serious limitations pervade existing empirical literature on sentencing disparities.First, studies focus on sentencing in isolation, controlling for the "presumptive sentence" or similar measures that themselves result from discretionary charging, plea-bargaining, and fact-finding processes.Any disparities in these earlier processes are excluded from the resulting sentence-disparity estimates.Our research has shown that this exclusion matters: pre-sentencing decision-making can have substantial sentence-disparity consequences.Second, existing studies have used loose causal inference methods that fail to disentangle the effects of sentencing-law changes, such as Booker, from surrounding events and trends.In contrast, we use a dataset that traces cases from arrest to sentencing, allowing us to assess Booker's effects on disparities in charging, plea-bargaining, and fact-finding, as well as sentencing.We disentangle background trends by using a rigorous regression discontinuity-style design.Contrary to other studies (and in particular, the dramatic recent claims of the U.S. Sentencing Commission), we find no evidence that racial disparity has increased since Booker, much less because of Booker.Unexplained racial disparity remains persistent, but does not appear to have increased following the expansion of judicial discretion.
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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 | 0.011 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".