Hate Speech, incitamento all’odio, incitación al odio: EU Parallel Corpora, Legal Discourse, Metadiscourse and Translation
Bibliographic record
Abstract
According to Sandrelli (2019, p. 111), “the multilingual co-drafting process produces equally authentic language versions of the same document in all the EU official languages. However, what actually happens in practice is that EU legislation is drafted in one language (English, in most cases) and is then translated into all the other ones”. Starting from this assumption, the aim of this paper is to investigate a series of hate speech-related EU documents in order to explore certain features of hate discourse and hate discourse-related phraseology, metadiscourse and translation issues in the English, Italian and Spanish versions of the texts. The quantitative and qualitative analysis will look at the use of peculiar language constructions in the three languages in relation, among other features, to hateful rhetoric, discrimination, violent behaviour, intolerance, harassment, gender inequalities, extremism and racism. Additionally, the features of metadiscourse (Hyland, 2019 [2005]) will be scrutinised in the three languages in order to ascertain whether and to what extent they function as rhetorical markers conferring a persuasive rather than merely an informative and prescriptive character to the texts under consideration. The parallel corpora include documents which date back to 2021. They appear to have as their underlying aim that of disseminating and circulating hate discourse-related counteractions, good practices and procedures in controversial cultural contexts and environments, especially those associated with such divisive matters as the safeguarding of human rights and human dignity of diverse religious, ethnic and social groups.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".