Bibliographic record
Abstract
This dissertation is composed of three papers broadly examining the relationship between the mass media and political polarization in the United States. The first paper examines whether the media might have played a role in the polarization of the American public. Using an automated content analysis of almost 600,000 news articles and transcripts from a variety of prominent news media sources over the past four decades, the paper analyzes whether coverage of ten issues has changed over time along several dimensions of tone (affect, incivility, conflict) and source cues (in particular, whether the media cover increasingly more extreme politicians). The results indicate that the media likely contributed to the process of partisan sorting by increasingly providing the public with partisan signals in the news coverage. There is also some evidence that the media contributed to the affective polarization of the public. The second paper focuses on the nature of media coverage of climate change and its effect on public opinion polarization of climate change attitudes, finding that despite the common perception, the media, including conservative media, did not overwhelmingly promote climate change skeptics, industry groups, or denialist organizations. Instead, the coverage featured an increasing number of partisan cues as the issue rose in salience, which polarized the public. In the third paper, I examine the relationship between climate change attitudes and news media diets. Previous work has focused extensively on Fox News and posits that Fox has been a dominant player in turning the Americans, and especially Republicans, into climate skeptics. Utilizing a large national survey, I find that the relationship is more nuanced than previously argued. Fox News does seem to have a negative effect on supporting governmental action in reducing greenhouse gas emissions, though that effect is limited to a small group of purists stuck in the conservative echo chamber. Most people, and importantly, most Republicans, are not very likely to be members of that group.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".