Sidestepping Deference: How United States v. Ressam Encourages Overly Stringent Review of Sentencing Decisions
Bibliographic record
Abstract
Ahmed Ressam, an Algerian national, traveled from France to Montreal in 1994 "using an illegally altered French passport."8 Although Canadian authorities intercepted him, "[a] moratorium on deportations from Canada to Algeria" permitted Ressam to remain in Canada.9 In 1998 he traveled to Afghanistan under a fake name and underwent terrorist training in light weapons, explosives, sabotage, and urban warfare.10 He returned to Canada in 1999 under directions to attack American interests before the end of the year; he chose to target LAX, one of the busiest American airports.11 In November 1999, Ressam traveled from Montreal to British Columbia, where he prepared explosives for the LAX bomb and hid them along with other explosive components in the wheel well of a rental car's trunk.12 He then entered the United States with the rental car via ferry from British Columbia to Port Angeles, Washington.13 When customs inspectors searched the car upon his arrival in Port Angeles, Ressam fled and "attempted to carjack a vehicle" before the inspectors apprehended him and discovered the hidden explosives.14 After a jury convicted him on "nine counts relating to his attempt to carry out an act of terrorism transcending a national boundary," Ressam agreed to cooperate with U.S. law enforcement officers investigating terror-related activities in exchange for a potential downward adjustment of his sentence.15 Ressam provided information leading to a twenty-four-year prison sentence for Mokhtar Hauoari, one of Ressam's co-conspirators, 16 and the
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".