Bibliographic record
Abstract
Other chapters in the present volume explore sentencing factors in jurisdictions such as South Africa, Canada or New Zealand, where courts impose sentence in the absence of formal guidelines. Across the United States, however, many jurisdictions employ formal sentencing guidelines, often in the form of a two-dimensional sentencing grid. These guidelines establish a ‘presumptive’ range of sentence for all offences. One of the most well-known and often studied systems is found at the federal level and applies to all offenders sentenced in the federal courts. The US Sentencing Commission issues these guidelines. The federal guidelines employ a two-dimensional grid (offence seriousness; criminal history) with 256 cells that delineates six-month sentencing ranges. Since their adoption in 1987, the US federal sentencing guidelines have required the use of narrow mandatory sentencing ranges in almost every case, thereby contributing to the creation of one of the largest prison populations in the world (Luna 2005; Stuntz 2001). In determining ranges the mandatory sentencing guidelines identified which aggravating factors must be considered while severely restricting the consideration of mitigating factors at sentencing, disfavouring most potential mitigating factors (Tonry 1996).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".