The Regulation of Hateful and Hurtful Speech: Liberalism’s Uncomfortable Predicament
Bibliographic record
Abstract
The regulation of speech is a highly sensitive and always evolving ethical, political, and legal issue. On the one hand, hateful and hurtful speech is on the rise, especially, but not exclusively, with regard to the relationship between Islam and the West. We can also think of the radicalization of discourse brought about by the interactive phase of the Internet. On the other hand, demands for the suppression of certain forms of speech proliferate. After reviewing the argument for freedom of expression, I argue that while the notion of harm defended by Millian liberals is too narrow, an “offence principle” is too broad. After defending hate speech laws, I concede that such laws need to target only the speech acts that express the most severe forms of aversion and denigration toward the members of a specific group. I then reflect on the status of “hurtful speech”, which I see as including the performative utterances that stop short of being hateful but nonetheless erode, through their illocutionary force and perlocutionary effects, the social standing and bases for self-respect of those who are targeted. I conclude that the free speech debate reveals a limit of liberal political morality and leaves liberal normative theorists with an uncomfortable predicament, as they have to rely more on the complementary role of pro-social personal dispositions and civic virtues than they generally wish to.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.087 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".