Bibliographic record
Abstract
Dark humour, i.e. humour engaging with sinister or distressing topics, is often at the centre of complex legal cases regarding freedom of expression and its limits. On the one hand, this type of humour is safeguarded in principle by international free speech standards, which should also apply to expressions that “offend, shock or disturb the State or any sector of the population” (as emphasised by the European Court of Human Rights in Handyside v. United Kingdom). On the other hand, jokes about terrorism, natural disasters or other highly sensitive subjects are frequently taken to court under charges such as incitement to discrimination, hostility or violence. Even within the same judicial system, the courts’ approach to these cases is often unpredictable, and would arguably benefit from a more nuanced perspective on dark humour as a form of expression. While acknowledging the inherently subjective nature of humour interpretation, this article proposes an indicative distinction between three basic genres of dark humour, or three potential outcomes of the interpretive process—namely ‘disparaging’, ‘sarcastic’, and ‘taboo-breaking’ dark humour. This theoretical framework is subsequently illustrated through the analysis of relevant legal cases from different jurisdictions, including the European Court of Human Rights and domestic courts from Spain, Italy, Belgium, Scotland and Canada. In conclusion, moving beyond the specific case of dark humour, this article outlines the potential benefits of a closer dialogue between humour research and judicial practice.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".