Learning From Others: The Scalia-Breyer Debate and the Benefits of Foreign Sources of Law to U.S. Constitutional Interpretation of Counter-Terrorism Initiatives
Bibliographic record
Abstract
Abstract The article discusses the importance of using foreign sources in the constitutional interpretation of counter-terrorism initiatives. By reviewing the Arar case, the author underlines the value of Canadian jurisprudence to evaluating extraordinary rendition. The similarities between the Arar and El Masri cases underscore the weakness of the current standard adopted by American judiciary in evaluating extraordinary rendition. The article further draws on the Israeli experience in dealing with torture. American jurisprudence will be greatly improved if it directly discusses the strengths and weaknesses of the Israeli debate in its own constitutional interpretation. In totality, American jurisprudence has an incredible untapped resource in the experiences of other countries on addressing problems that Americans would likely see as unique.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".