Unraveling Media Dynamics: Investigating Societal Perspectives via Eike Batista’s Case in Brazil
Bibliographic record
Abstract
This study provides a detailed analysis of the societal roles assigned to Eike Batista by various media platforms throughout his career. By examining newspapers, magazines, radio, television, and social media, the research explores how these diverse communication channels influence public perceptions and highlight critical issues. Media narratives play a crucial role in shaping societal views, affecting the collective imagination, and either reinforcing existing social norms or driving transformative changes. The study highlights how media representations can impact individual and collective identities by constructing and deconstructing public images. The research underscores the media’s significant role in shaping contemporary societal dynamics, emphasizing the need to understand how media-disseminated information influences public perception and the institutionalization of new practices. By analyzing media portrayals of Batista, the study offers insights into the ways media narratives contribute to the construction of societal roles and perceptions. Furthermore, the study addresses a gap in organizational institutional theory by exploring how media narratives affect both collective and individual identities, revealing the media’s influence on societal change. The research aims to provide a comprehensive understanding of the impact of media on social roles and societal norms through the specific case of Eike Batista.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".