News media impact on sociopolitical attitudes
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
In the present project we assessed whether partisan news affects consumers' views on polarizing issues. In Study 1 nationally representative cross-sectional data (N = 4249) reveals that right-leaning news consumption is associated with more right-leaning attitudes, and left-leaning news consumption is associated with more left-leaning attitudes. Additional three-wave longitudinal data (N = 484) in Study 2 reveals that right-leaning news is positively (and left-leaning news is negatively) associated with right-leaning issue stances three months later, even after controlling for prior issue stances. In a third (supplemental) study (N = 305), random assignment to right-leaning (but not left-leaning) news (vs. control) experimentally fostered more right-leaning stances, regardless of participants' previously held political ideology. These findings suggest that partisan news, and particularly right-leaning news, can polarize consumers in their sociopolitical positions, sharpen political divides, and shape public policy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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