The First Amendment on Trial: Hate Speech, Free Speech and College Campuses
Bibliographic record
Abstract
This thesis examines the tension between the First Amendment’s guarantee of free speech and the harmful impact of hate speech on college campuses. As incidents of targeted, discriminatory rhetoric rise in academic settings, the need to reevaluate what constitutes protected speech has become increasingly urgent. Through legal analysis, including case law such as Brandenburg v. Ohio and Schenck v. United States, this paper argues that the Supreme Court must distinguish between constitutionally protected speech and harmful hate speech. The thesis uses contemporary case studies from college campuses across the country. It includes firsthand accounts from the University at Albany to demonstrate how hate speech, particularly racially and religiously motivated rhetoric, has compromised students’ emotional, psychological, and physical safety. It critiques the inaction of the Supreme Court and compares American policy to Canada’s precedent in R. v. Keegstra, suggesting that legal definitions and limitations on hate speech are both necessary and possible. The paper concludes by calling for the Supreme Court to modernize its interpretation of the First Amendment to ensure that free speech no longer enables discrimination and harassment in academic spaces.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| 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".