Construção dos ethè em discursos políticos em Portugal e no Brasil:: um estudo comparativo
Bibliographic record
Abstract
The concept of ethos was initially used by Aristotle in his rhetorical studies. Since then, several authors recovered the concept in their linguistic-textual and discursive studies. Ducrot addressed the notion of ethos concerning the distinction between speakers λ and L. Maingueneau associates the ethos to the speaker’s statute and to the process of legitimating what it says. Charaudeau also relates ethos with the speaker, linking it to his social, moral and ideologicalrepresentations. This contribution, corresponding to an excerpt of a doctoral thesis, aims at identifying the ethè built by Portuguese and Brazilian statesmen in the “year-end” political messages of 2011 and 2012 and analyze its linguistic and textual materiality. To achieve this goal, we conducted a linguistic analysis focused on speech acts (Searle), on Enunciative Responsibility marks (Adam, 2008) and on lexical-textual structures. Preliminary results indicate the existence of specific ethè of each statesman, as well as common ethè, which have similarities and differences regarding the linguistic realization. Given these results, raises the hypothesis of these differences, whether of ethè or materiality, are due to cultural issues.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".