Bibliographic record
Abstract
This study examines the dynamics of hate speech on social media, focusing on comments opposing the announcement of the Queermuseu – Cartografias da Diferença na Arte Brasileira (Cartographies of Diversity in Brazilian Art) exhibition in southern Brazil. Employing appraisal system, Systemic-Functional Linguistics, and network analysis, the research investigates the linguistic and semantic patterns underlying the interactions. Lexical and interaction networks were qualitatively interpreted in order to understand the interaction amongst users and the reaction towards such exhibition. Results reveal that conservative discourse significantly drives comments towards negative evaluation, framing the exhibition as a threat to religious and traditional family values. This research contributes to understanding the role of digital platforms in amplifying intolerance and the mechanisms of polarized discourse in Brazilian Portuguese. Cette étude examine la dynamique des discours de haine sur les réseaux sociaux, en se concentrant sur les commentaires opposés à l'annonce de l'exposition Queermuseu – Cartografias da Diferença na Arte Brasileira (Cartographies de la Diversité dans l'Art Brésilien) dans le sud du Brésil. En utilisant le système d'appréciation, la Linguistique Systémique-Fonctionnelle et l'analyse des réseaux, la recherche explore les schémas linguistiques et sémantiques sous-jacents aux interactions. Les réseaux lexicaux et interactionnels ont été interprétés qualitativement pour comprendre les interactions entre les utilisateurs et les réactions face à cette exposition. Les résultats montrent que le discours conservateur alimente largement les commentaires à travers une évaluation négative, présentant l'exposition comme une menace pour les valeurs religieuses et familiales traditionnelles. Cette recherche contribue à la compréhension du rôle des plateformes numériques dans l'amplification de l'intolérance et des mécanismes de discours polarisés en portugais brésilien.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.812 | 0.725 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".