Ambivalent Resistance and Comparative Constitutionalism: Opening up the Conversation on "Proportionality," Rights and Federalism
Bibliographic record
Abstract
This article concerns what I will call the ambivalent resistance of U.S. constitutional law to explicit learning and borrowing from other nations' constitutional decisions and traditions.It begins in Part I by identifying this resistance and explaining in what respects it is ambivalent, and goes on in Part II to suggest reasons for the resistance and for why it may diminish in the future.To explore the attractions and dilemmas of comparative constitutional law, Part III examines how a particular doctrine found in Canadian constitutional law, the so-called "proportionality" test of R. v. Oakes,' might bear on recent constitutional issues of federalism and individual rights in the United States.This section argues for the value of studying foreign constitutional law, while at the same time urging caution about the possibility of direct "transplants."Finally, the article explores the relationship between the U.S. Supreme Court's resistance to considering foreign constitutional law and its resistance to input from other domestic institutions, notably Congress, on the meaning of the U.S. Constitution.It argues for a broader understanding of what is relevant to U.S. constitutional interpretation, embracing both congressional judgments and judgments reached by the constitutional courts of other nations considering similar problems..
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".