Beyond Sex and Saxophones: Interviewing Practices and Political Substance on Televised Talk Shows
Bibliographic record
Abstract
The goal of this paper is to assess the contribution of infotainment and entertainment television talk shows by comparing political interviews on these TV shows with current affairs programs. Few political scientists have examined political interviews, in general, and political interviews on entertainment outlets, in particular. Moreover, these studies are often focused on the sorts of topic participants talk about in such programs. On the basis of literature developed by scholars in sociolinguistics and journalism, we expand the scope of our study to the assessment of questions asked by the interviewers and answers provided by the politicians. We perform a quantitative content analysis of political interviews to compare the behavior of these speakers on infotainment and entertainment programs with those on current affairs programs. Our results show that hosts on infotainment programs are no less rigorous than their counterparts on information programs, especially when the interview is centered on policy issues. We conclude that scholars interested in these questions should turn to studies in sociolinguistics and journalism to build a relevant analytical frame.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".