Talk Radio’s America: How an Industry Took Over a Political Party That Took Over the United States—Brian Rosenwald (Cambridge, MA, USA: Harvard Univ. Press, 2019, 358 pp.)
Bibliographic record
Abstract
In March 2020,as the coronavirus was rapidly spreading throughout the United States, President Trump strode into the Situation Room for a meeting with his COVID-19 task force. According to sources in attendance, the President excitedly announced that he wanted to start a 2-hour, daily White House talk radio show to provide a regular opportunity for him to update Americans, quell fears, and answer listener questions. Ultimately, the President quashed the idea, giving as his reason that it would compete with Rush Limbaugh, whose legendary talk radio program was the gold standard among conservative supporters. When aids suggested the White House program might air at a time that did not conflict with Limbaugh’s broadcast, the President demurred, choosing not to ruffle the feathers of right-wing radio’s Big Bird. The talk radio maestro was held in such high regard that, just a month or so earlier, the President used the solemn occasion of his State of the Union address to announce that he was giving Mr. Limbaugh the nation’s highest civilian award, the Presidential Medal of Freedom. This decision would not surprise Brian Rosenwald whose book documents Limbaugh’s formative role in turning an old technology into an instrument of power that transformed the Republican Party and political discourse in the United States.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".