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.)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In March 2020,</b> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it