Memories of the Limbaugh Administration 1990s Politics, Conservative Media, and <i>Infinite Jest </i>as a Novel of Radio
Bibliographic record
Abstract
This essay addresses the questions of why conservative radio host Rush Limbaugh appears in the background ofInfinite Jestas pre-Subsidized Time president and what this small, unexplained detail opens up about the relationship of the novel and some of Wallace’s nonfiction writing to conservative media, the later rise of Donald Trump, and the role of radio in particular in Wallace’s imagination of narrative voice. While there are some things to say about Trump and aboutInfinite Jest’s seeming prediction of his presidency, in the figure of entertainer-turned-politician Johnny Gentle, this essay focuses instead on Wallace’s concrete (if fleeting) prediction of President Limbaugh, as well as the larger role Limbaugh’s strident, cruel, demagogic voice played in the way Wallace (a heavy listener to all sorts of radio) imagined political, media, and even fiction-writing possibilities. The essay examines how Limbaugh as major conservative media figure lies not just in the background of Infinite Jestbut is threaded through Wallace’s analysis of U.S. politics and potential fascism, from the early 1990s in whichInfinite Jestwas largely composed through to one of Wallace’s final major essays, the 2005 account of conservative talk radio titled “Host” are analysed. In close-readings ofInfinite Jest, sonic elements of Infinite Jest and certain subliminal anti-Limbaugh and anti-Gentle agendas in the radio host the novel does describe at length, Joelle van Dyne, a.k.a. Madame Psychosis, adding much-needed nuance to analyses of Wallace that focus exclusively on visual media as dominant forces in U.S. culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".