Border Justice? Sentencing Federal Narcotics Offenders in Southwest Border Districts: A Focus on Citizenship Status
Bibliographic record
Abstract
The war on drugs has largely been waged in the southwestern border region of the United States. Five federal border districts (California South, Arizona, New Mexico, Texas West, and Texas South) alone are responsible for roughly one quarter of federal narcotics prosecutions annually. Narcotics cases make up roughly 30% of the federal criminal caseload each year, and the number of Hispanic and noncitizen defendants prosecuted in U.S. federal courts has risen steadily over the past two decades. In 1991, noncitizens comprised about 23% of persons prosecuted in federal courts; by 2009, nearly 45% of those prosecuted were noncitizens.This study examined judicial sentencing practices for federal narcotics offenders in these five southwestern border districts to explore the effect of citizenship status on sentence length. We also partition our analysis by district to enable assessment of variation in sentencing practices across these federal border districts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".