Towards and Explicit Balancing Inquiry - R.A.V. and Black through the Lens of Foreign Freedom of Expression Jurisprudence
Bibliographic record
Abstract
In this Note, the author considers the Supreme Court's recent First Amendment jurisprudence-in particular R.A.V. v. City of St. Paul and Virginia v. Black, two cases concerning legislative attempts to criminalize cross-burning-through the eyes of foreign freedom of speech jurisprudence. He argues that the freedom of speech adjudicative models provided by the European Court of Human Rights and the Canadian Constitutional Court not only suggest a clearer mode of First Amendment adjudication, but may also enable us to more clearly understand the nature of the emergent case-by-case balancing jurisprudence implicitly present in both R.A.V. and Black.\nPart I of the Note briefly considers how, despite its textual clarity and unequivocal command, First Amendment fundamentalism has been rejected by the Supreme Court. Part II introduces the motivation and means by which the European Court of Human Rights and the Canadian Constitutional Court adopted and utilize case-by-case balancing tests in freedom of expression adjudication. Part III examines R.A.V. and Black, revealing the extent to which such case-by-case analysis already informs modern First Amendment jurisprudence. Finally, in Part IV the author proposes a case-by-case First Amendment balancing test informed by precedent and foreign adjudicative models, and responds to several anticipated objections to such a test.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| 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".