Bibliographic record
Abstract
An investigation of hate speech: legal approaches, current controversies, and suggestions for limiting its spread. Hate speech can happen anywhere—in Charlottesville, Virginia, where young men in khakis shouted, “Jews will not replace us”; in Myanmar, where the military used Facebook to target the Muslim Rohingya; in Capetown, South Africa, where a pastor called on ISIS to rid South Africa of the "homosexual curse.” In person or online, people wield language to attack others for their race, national origin, religion, gender, gender identity, sexual orientation, age, disability, or other aspects of identity. This volume in the MIT Press Essential Knowledge series examines hate speech: what it is, and is not; its history; and efforts to address it. Author Caitlin Ring Carlson, an expert in communication and mass media, defines hate speech as any expression—spoken words, images, or symbols—that seeks to malign people for their immutable characteristics. Hate speech is not synonymous with offensive speech—saying that you do not like someone does not constitute hate speech—or hate crimes, which are criminal acts motivated by prejudice. Hate speech traumatizes victims and degrades societies that condone it. Carlson investigates legal approaches taken by the EU, Brazil, Canada, Germany, Japan, South Africa, and the United States, with a detailed discussion of how the U.S. addresses, and in most cases, allows, hate speech. She explores recent hate speech controversies, and suggests ways that governments, colleges, media organizations, and other organizations can limit the spread of hate speech.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".