Exploring Discrimination Online: A Critical Discourse Analysis of New Forms of Hate Speech Against Gypsy, Roma and Traveller Communities
Bibliographic record
Abstract
In August 2023, a leaflet titled “Gypsy and Traveller site coming to your area soon!” was distributed in the Welsh county of Monmouthshire regarding the development of a Gypsy and Traveller site. The leaflet asked people to fill out a form to determine their opinion on the proposal to establish sites in the area. Firstly, this article will focus on newspaper comment sections to look for instances of hate speech against the Gypsy, Roma and Traveller Communities, but also to analyse readers’ responses to the articles about this issue and to explore how meanings are produced when readers react to the news reports, drawing on van Dijk’s theory of Sociocognitive Discourse Studies (2016). A sample of comments posted by readers on the websites of some British tabloid newspapers has been collected in order to analyse the connection between discursive and social structures. Secondly, the article will determine how the main social actors are represented in the comments, adopting the analytical tools drawn from Critical Discourse Analysis and van Leeuwen’s (1996) inventory of socio-semantic categories and modes of representation.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".