U.S. Territorial Exclusion in Federal Sentencing Research: Can it be Justified?
Bibliographic record
Abstract
There is a dearth of knowledge on sentencing process and outcomes in Guam, the Northern Mariana Islands, Puerto Rico and the United States Virgin Islands. It is not uncommon for researchers conducting national studies to intentionally exclude data from these American territories. Their actions have been justified on the grounds that territories have "distinctive" characteristics that warrant exclusion. Using federal sentencing data, this study explores whether the sentencing patterns observed in the territories are as "unique" as scholars assume and if so, in what ways and to what extent. Descriptive analysis reveals that attributes of offenders and case processing strategies are similar across the U.S. mainland and its territories. Although multivariate analysis revealed some notable differences (e.g., territories are more punitive than states) the larger finding is that there are more similarities than differences with regards to the processing of cases and outcomes. Implications of the study and directions for future research are discussed.
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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 | 0.068 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".