Bibliographic record
Abstract
Abstract We are at a notable moment to contemplate federal sentencing. Fifteen years ago, the Supreme Court issued its landmark decision in United States v. Booker. Just over 25 years ago, Congress passed and the President signed the 1994 Crime Bill. By looking backward and learning from history, we may be able to move forward more productively. One remarkable aspect of Booker is that it still controls federal sentencing a decade and a half later. Congress has chosen to largely leave the system as the Court refashioned it. The world is different today than it was in 2005. Yet the Booker framework – established by two essentially dueling 5-4 majorities of the Supreme Court – endures. In some ways, the most remarkable aspect of Booker at 15 is how unremarkable it appears to contemporary eyes. It is the dog that doesn’t bark – at least not much. In order to truly benefit from the lessons of our criminal justice history, we must go beyond guidelines. Just over 25 years ago, Congress spoke forcefully in the 1994 Crime Bill. It was addressing the concerns of that era with tactics that garnered wide support at the time but are not always viewed favorably today. By stopping to explore the context and consequences of two of the most significant judicial and legislative criminal justice events of the last quarter-century, lessons may emerge. That is a good thing. If we are to make mistakes again (and we will), they should be new ones. Only by understanding the past can we effectively illuminate the path forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.022 | 0.042 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.017 | 0.038 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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".