Bibliographic record
Abstract
O objetivo deste trabalho é apresentar alguns resultados da análise de editoriais de dois jornais portugueses: Público e Diário de Notícias, a partir de conceitos de tipos de discurso e gênero (Bronckart, 1997), ethos dito e cenografia (Maingueneau, 1999). Considerando o editorial um gênero de predominância organizacional argumentativa e funcional persuasiva, a análise procura observar, a partir do levantamento de formas lingüísticas e/ou procedimentos lingüístico-textuais relevantes, a correlação entre os diversos tipos de discurso mencionados por Bronckart e a construção de vários ethè. Os resultados das análises preliminares apontaram uma heterogeneidade do ethos nos editoriais dos dois jornais. This article aims at presenting the analytical results regarding editorials of two dominant Portuguese newspapers: Público and Diário de Notícias, having in mind some concepts such as types of discourse and genders (Bronckart, 1997), ethos and discoursive “scenography”, developed by Maingueneau (1999). Defending the idea that an editorial is a gender composed of an argumentative organization and a persuasive function, the referred analysis tries to observe, through linguistic expressions and/or textual linguistic procedures, the relationship between the types of discourse presented by Bronckart and the construction of multiple ethè. The study provides some evidence of the ethos heterogeneity in the analyzed editorials of both newspapers.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".