President, Wrestler, Spectacle: An Examination of Donald Trump’s Firing Tweets and <i>The Celebrity Appresident</i> as Response to Trump’s Media Landscape
Bibliographic record
Abstract
Between January and April 2017, the Trump administration underwent a period of extensive cabinet turnover that was publicized via Donald Trump’s Twitter feed. Accessibility to such information creates a “break” with the mythos of a president and raises questions concerning the relationship between presidential decorum and social media usage. Trump’s Twitter usage operates as a platform to reinforce populist rhetoric and pushes his various spectacles (e.g., entertainment, business, and presidency) through the notion of “winners” and “losers.” This conflation of Trump’s positions enables his Twitter usage to mimic the Barthian mythos of the wrestler, an overly aggressive figure that borders the line between pantomime and entertainer. Twitter also enables a space for satirical response, specifically through The Daily Show with Trevor Noah’s The Celebrity Appresident. As such, the nexus of Trump’s twitter behavior and satirical response is indicative of the current political climate that combines myth, spectacle, and social media.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".