The Shaping Effects Of Grandstanding In The Media: Exploring Public Figure Joe Rogan’s Contributions To Public Dialogue On The Covid-19 Health Crisis
Bibliographic record
Abstract
This research paper proposes a framework to examine grandstanding employed by public figures who influence how citizens form opinions and make decisions. This paper applies a case study approach to explore how comedian and YouTube personality, Joe Rogan, contributed to the conversation on the COVID-19 pandemic. More specifically, this paper analyzes how his deliberations are reflected in the media and what effects such contributions have on healthy public dialogue. A qualitative content analysis was performed to analyze a sample of Rogan’s podcast transcripts on YouTube to detect instances of grandstanding, and a coding system was developed to categorize each video by its content. A quantitative analysis was also conducted to extend the study to examine media coverage and the effects of such behaviour on individuals. The findings provide insight into how Rogan fuels polarization and misinformation, and how such conduct stands in the way of the pursuit of truth. The results point to grandstanding as a successful strategy in status-seeking, gaining media traction, and making headlines. This study attempts to provide a clear indication of the threats of free-form discussion and exploratory dialogue on YouTube during COVID-19, as well as the effects of grandstanding in the media and beyond.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".