Is There a Political Bias? A Computational Analysis of Female Subjects' Coverage in Liberal and Conservative Newspapers
Why this work is in the frame
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Bibliographic record
Abstract
Objectives One possible source for the gap in media coverage between female and male subjects is the political affiliation of the media source. The objective of this present study was to test whether there is a difference between more liberal and more conservative newspapers in coverage rates of female subjects. Methods We used computational methods to analyze a unique large‐scale data set (complied by the Lydia Text Analysis System) and compared the 2010 female coverage rates in 168 newspapers. Results Contrary to our expectations, we found that conservative media tend to cover female subjects no less (and even slightly more) than liberal media. However, the difference was no longer significant once we controlled for newspaper distribution. Conclusion The common view that liberal newspapers are more likely to cover female subjects was not supported by this study. Both conservative and liberal newspapers are much more likely to cover males.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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