Is Gender a Driver of Topic Choice? A Comparative Keyword Analysis of Political Cable News Interviews
Bibliographic record
Abstract
Cable news networks have become an increasingly important source of political news in the United States. They wield considerable influence on public opinion, particularly in relation to current issues involving social roles and gender dynamics. This study offers insights into how the choice of topic in political cable news interviews may be influenced by the gender of participants. A corpus of 40 political cable news interviews was compiled and analyzed on the basis of various combinations of male and female interviewers and interviewees. Corpus software was implemented to extract keywords that were then grouped to identify prominent topics according to gender. Topics discussed exclusively among male participants were more issue oriented (i.e., immigration, healthcare, the economy, and gun control) as compared to those discussed exclusively among female participants that were more in social nature (i.e., personal matters, the Kavanaugh Supreme Court nomination, and tech giants in the context of social justice). Results showed that topics emerging from the female participants’ discourse were aligned with some widely held perceptions of women’s speech. At the same time, other features of the female participants’ speech appeared to be driven largely by their professional and institutional roles, and thus, not aligned with stereotypical perceptions. The findings have implications for the role of media and cable news in contemporary American society in avoiding the perpetration of gender-related topic bias.
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.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".