Flying chairs, heated takes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 2024 São Paulo mayoral election sparked intense political discourse, particularly following a highly publicized altercation during a live debate on September 15. The incident, in which candidate José Luiz Datena struck Pablo Marçal with a chair, led to widespread discussion on social media, particularly on YouTube. This study investigates the dynamics of online discourse surrounding this event, focusing on audience engagement and sentiment across five major YouTube news channels: UOL, Folha de São Paulo, CNN Brasil, Poder360, and Itatiaia. Using a discourse analysis approach adapted from Teixeira et al. (2018), we collected and categorized 500 top-ranking YouTube comments, classifying them into four primary categories: Humor, Support, Criticism and Protest, and Neutral. A second layer of analysis further refined support and criticism, differentiating between pro-Datena, pro-Marçal, and general political dissatisfaction. Our findings reveal that humor was the dominant response across all platforms, suggesting a tendency toward memefication and satire in Brazilian digital political discourse. However, significant polarization was observed, with Datena receiving both overwhelming support and the highest level of criticism across outlets. Media framing influenced audience reactions, as outlets with in-depth coverage fostered broader critiques, while those with shorter, sensationalist clips amplified polarized sentiments. This study contributes to research on political communication and social media discourse by demonstrating how digital platforms mediate political controversies and shape public perception. The results highlight the role of algorithmic content curation in reinforcing ideological divides and fostering emotionally charged interactions. By offering a systematic analysis of audience reactions, this study provides insights into the evolving nature of digital political engagement in Brazil and lays the groundwork for future research on media framing and discourse analysis in online environments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it