Ballot access case needs prompt, decisive action
- Bias Rating
- Reliability
65% ReliableAverage
- Policy Leaning
10% Center
- Politician Portrayal
-25% Negative
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Bias Score Analysis
The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
7% Positive
- Liberal
- Conservative
Sentence | Sentiment | Bias |
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
Liberal
100%
Conservative

Contributing sentiments towards policy:
56% : Lash argues that section 3 does not apply to the president, and that Congress, in enacting the Amendment, was instead more fearful that leaders of the Confederacy would use their state-level popularity to secure seats in Congress or government appointments.53% : As of the end of January, 51 amicus briefs (friend of the court) have been filed: 34 in support of the petitioner, Donald Trump; 4 in support of the respondent, Norma Anderson, a Colorado voter bringing the suit to deny Trump ballot access; and 13 in support of neither side.
46% : A brief on behalf of petitioner Trump was filed by Kurt T. Lash, a constitutional law professor at the University of Richmond School of Law and a preeminent expert on the history of the 14th Amendment, including section 3.
45% : She argues that the central questions "must be fully answered now" because state election officials and voters "deserve to know whether [Trump] is eligible to appear on the ballot as a candidate before casting their votes" in the primary and general elections.
41% : She has ruled against a group of citizens who brought suit to exclude Trump from the ballot, and her decision has been upheld by a state appeals court and its Supreme Court.
34% : The U.S. Supreme Court Thursday will hear oral arguments in Trump v. Anderson.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.