Bibliographic record
Abstract
Proportionality, accepted as a general principle of constitutional law by many countries, requires that government intrusions on freedoms be justified, that greater intrusions have stronger justifications, and that punishments reflect the relative severity of the offense.Proportionality as a doctrine developed by courts, as in Canada, has provided a stable methodological framework, promoting structured, transparent decisions even about closely contested constitutional values.Other benefits of proportionality include its potential to bring constitutional law closer to constitutional justice, to provide a common discourse about rights for all branches of government, and to help identify the kinds of failures in democratic process warranting heightened judicial scrutiny.Earlier U.S. debates over "balancing" were not informed by recent comparative experience with structured proportionality doctrine and its benefits.Many areas of U.S. constitutional law include some elements of what is elsewhere called proportionality analysis.I argue here for greater use of proportionality principles and doctrine; I also argue that proportionality review is not the answer to all constitutional rights questions.Free speech can benefit from categorical presumptions, but in their application and design proportionality may be relevant.The Fourth Amendment, which secures a "right" against "unreasonable searches and seizures," is replete with categorical rules protecting police conduct from judicial review; more case-by-case analysis of the "unreasonableness" or disproportionality of police conduct would better protect rights and the rule of law."Disparate impact" equality claims might be better addressed through more proportionate review standards; Eighth Amendment review of prison sentences would benefit from more use of proportionality principles.Recognizing proportionality's advantages, and limits, would better enable U.S. constitutional law to at once protect rights and facilitate effective democratic self-governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".