Retrato das manifestações de rua no processo de impeachment da presidenta Dilma Rousseff: a construção da opinião pública pela mídia privada brasileira
Bibliographic record
Abstract
ResumoEste artigo trata da cobertura jornalística realizada pela mídia comercial brasileira sobre as manifestações favoráveis e contrárias ao impeachment da presidenta Dilma Rousseff, no decorrer de 2015 e início de 2016.Observou-se uma cobertura da imprensa privada favorável às manifestações pró-impeachment, com abertura de páginas para o evento, imagens aéreas, fotos abertas em grandes proporções, além dos destaques para os números de manifestantes, como forma de sugerir amplo apoio da maioria da população à deposição da presidenta.E contrapartida, os protestos favoráveis ao governo Dilma tiveram cobertura enxuta, sem ênfase nas primeiras páginas e aos números, indicando pequena participação e reduzido apoio da população à presidenta.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".