Bibliographic record
Abstract
Hardly a day goes by without another legal controversy somewhere in the world regarding the regulation or suppression of hate speech. The problem is not only topical and current, it is increasingly problematic. Hatred, racism, xenophobia and related forms of intolerance have gone global, moving from promoting hatred against individuals and groups within states, to singling out these groups for discriminatory and differential treatment everywhere. The globalization of hatred includes the additional feature of terrorist activity—the use of hate propaganda in recruitment efforts, the expansion of target groups, and the specific targeting of human rights defenders for violence and harassment. At the same time, the use of the Internet to incite violent crime and promote hatred has increased exponentially in the past 15 years. On the one hand, there is the marketplace of ideas approach, which posits that the solution for the hate speech problem is more speech. It flies in the face of the lived reality of victims of hate propaganda (such as survivors of genocide and other mass human rights violations) who understand that expression in an unregulated marketplace of ideas is used to the detriment of the search for the truth by undermining rationality and promoting deadly forms of intolerance, prejudice and violence. On the other hand, others are calling for increased limits on expression including the introduction of blasphemy laws to protect certain religious ideas from criticism, even when those ideas may promote hatred. The paper argues that the debate should be framed in terms of equality of citizenship, protection of speech rights for minorities and prevention of harm to individuals, not ideas. At the same time, a non-discriminatory understanding on the limits of expression must be found that avoids privileging of certain cultural and racial perspectives in the marketplace of ideas. The challenge is to develop protection for freedom of expression informed by principles of human dignity, equality and the prevention of harm. The Canadian Supreme Court has developed a unique and important jurisprudence in this area which should be considered.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".