A Computational Framework for Media Bias Mitigation
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
Bias in the news media is an inherent flaw of the news production process. The bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as the Iraq war. This article presents NewsCube, a novel Internet news service which aims to mitigate the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified views on a news event. As such, it helps readers understand the event from a plurality of views and to formulate their own, more balanced, viewpoints. The media bias problem has been studied extensively in mass communications and social science. This article reviews related mass communication and journalism studies and provides a structured view of the media bias problem and its solution. We propose media bias mitigation as a practical solution and demonstrate it through NewsCube. We evaluate and discuss the effectiveness of NewsCube through various performance studies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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